diff --git a/model/loss.py b/model/loss.py
new file mode 100644
index 0000000..ac29a19
--- /dev/null
+++ b/model/loss.py
@@ -0,0 +1,18 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GMMLoss(nn.Module):
+    def __init__(self):
+        super(GMMLoss, self).__init__()
+
+    def forward(self, x, mu, std, pi):
+        x = x.unsqueeze(-1)
+        std = std + 1e-6
+        distrib = (1.0 / np.sqrt(2*np.pi)) * torch.exp(-0.5 * ((x - mu) / std) ** 2) / std
+        distrib = torch.sum(pi * distrib, dim=3) + 1e-6
+        loss = -1.0 * torch.log(distrib) # NLL
+        
+        return torch.mean(loss)
diff --git a/model/model.py b/model/model.py
new file mode 100644
index 0000000..0ddcc90
--- /dev/null
+++ b/model/model.py
@@ -0,0 +1,77 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .tier import Tier
+from .loss import GMMLoss
+from utils.constant import f_div, t_div
+
+
+class MelNet(nn.Module):
+    def __init__(self, hp):
+        super(MelNet, self).__init__()
+        self.hp = hp
+        self.f_div = f_div[hp.n_tiers+1]
+        self.t_div = t_div[hp.n_tiers+1]
+
+        self.tiers = nn.ModuleList([None] +
+                                   [Tier(hp=hp,
+                                         freq=hp.n_mels // self.f_div * f_div[tier],
+                                         layers=hp.layers[tier-1],
+                                         tierN=tier)
+                                    for tier in range(1, hp.n_tiers+1)])
+
+    def forward(self, x, tier_num, text=None):
+        assert tier_num > 0, 'tier_num should be larger than 0, got %d' % tier_num
+
+        mu, std, pi = self.tiers[tier_num](x, text)
+        return mu, std, pi
+
+    
+       
+    def interleave(self, tier_a, tier_b, axis):
+        assert axis in ['time', 'freq'], "Axis shoud be either time or frequncy"
+        
+        if axis=='time':
+            output = tier_a.new_zeros(tier_a.size(0), tier_a.size(1), 2*tier_a.size(2))
+            for i in range(tier_a.size(2)):
+                output[:,:,2*i] = tier_a[:,:,i]
+                output[:,:,2*i+1] = tier_b[:,:,i]
+        
+        elif axis=='freq':
+            output = tier_a.new_zeros(tier_a.size(0), 2*tier_a.size(1), tier_a.size(2))
+            for i in range(tier_a.size(1)):
+                output[:,2*i,:] = tier_a[:,i,:]
+                output[:,2*i+1,::] = tier_b[:,i,:]
+                
+            return output
+        
+        
+        
+    def sample(self, n_sec, text=None, device=None):
+        #autoregressively compute tier1
+        n_slice = n_sec * self.hp.sr 
+        x = torch.zeros(1, self.hp.n_mels//self.f_div, n_slice//self.t_div)
+        
+        if device is not None:
+            x = x.to(device)
+        
+        tier1 = self.tiers[1].sample(x, text)
+        tier2 = self.tiers[2].sample(tier1)
+        tier1_2 = self.interleave(tier1, tier2, axis='freq')
+        
+        tier3 = self.tiers[3].sample( tier1_2 )
+        tier1_2_3 = self.interleave(tier1_2, tier3, axis='time')
+        
+        tier4 = self.tiers[4].sample( tier1_2_3 )
+        tier1_2_3_4 = self.interleave(tier1_2_3, tier4, axis='freq')
+        
+        tier5 = self.tiers[5].sample( tier1_2_3_4 )
+        tier1_2_3_4_5 = self.interleave(tier1_2_3_4, tier5, axis='time')
+        
+        tier6 = self.tiers[6].sample( tier1_2_3_4_5 )
+        
+        output = self.interleave(tier1_2_3_4_5, tier6, axis='freq')
+            
+        return output
diff --git a/model/rnn.py b/model/rnn.py
new file mode 100644
index 0000000..3b3b8e4
--- /dev/null
+++ b/model/rnn.py
@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .stack_c import Stack_C
+from utils.constant import f_div
+
+
+class DelayedRNN(nn.Module):
+    def __init__(self, hp, tierN):
+        super(DelayedRNN, self).__init__()
+        self.TTS = hp.TTS
+        self.num_hidden = hp.hidden_dim
+        self.tierN = tierN
+
+        self.freq = hp.n_mels * f_div[tierN] // f_div[hp.n_tiers+1]
+
+        self.t_delay_RNN_x = nn.LSTM(input_size=self.num_hidden, hidden_size=self.num_hidden, batch_first=True)
+        self.t_delay_RNN_yz = nn.LSTM(input_size=self.num_hidden, hidden_size=self.num_hidden, batch_first=True, bidirectional=True)
+
+        # use central stack only at initial tier
+        if (tierN == 1):
+            self.c_RNN = Stack_C(hp)
+            
+        self.f_delay_RNN = nn.LSTM(input_size=self.num_hidden, hidden_size=self.num_hidden, batch_first=True)
+
+        self.W_t = nn.Linear(3*self.num_hidden, self.num_hidden)
+        self.W_c = nn.Linear(self.num_hidden, self.num_hidden)
+        self.W_f = nn.Linear(self.num_hidden, self.num_hidden)
+
+    def forward(self, input_h_t, input_h_f, input_h_c=0.0, memory=None):
+        # input_h_t, input_h_f: [B, M, T, D] / input_h_c: [B, T, D]
+        B, M, T, D = input_h_t.size()
+
+        ####### time-delayed stack #######
+        # Fig. 2(a)-1 can be parallelized by viewing each horizontal line as batch
+        h_t_x, _ = self.t_delay_RNN_x(input_h_t.view(-1, T, D))
+        h_t_x = h_t_x.view(B, M, T, D)
+
+        # Fig. 2(a)-2,3 can be parallelized by viewing each vertical line as batch,
+        # using bi-directional version of LSTM
+        temp = input_h_t.transpose(1, 2).contiguous() # [B, T, M, D]
+        temp = temp.view(-1, M, D)
+        h_t_yz, _ = self.t_delay_RNN_yz(temp)
+        h_t_yz = h_t_yz.view(B, T, M, 2*D)
+        h_t_yz = h_t_yz.transpose(1, 2)
+
+        h_t_concat = torch.cat((h_t_x, h_t_yz), dim=3)
+        output_h_t = input_h_t + self.W_t(h_t_concat) # residual connection, eq. (6)
+
+        ####### centralized stack #######
+        output_h_c = 0.0
+        h_c_expanded = 0.0
+        if self.tierN == 1:
+            h_c_temp, _ = self.c_RNN(input_h_c, memory)
+            output_h_c = input_h_c + self.W_c(h_c_temp) # residual connection, eq. (11)
+            h_c_expanded = output_h_c.unsqueeze(1).repeat(1, self.freq, 1, 1)
+            
+            
+        ####### frequency-delayed stack #######
+        h_f_sum = input_h_f + output_h_t + h_c_expanded
+        h_f_sum = h_f_sum.transpose(1, 2).contiguous() # [B, T, M, D]
+        h_f_sum = h_f_sum.view(-1, M, D)
+
+        h_f_temp, _ = self.f_delay_RNN(h_f_sum)
+        h_f_temp = h_f_temp.view(B, T, M, D)
+        h_f_temp = h_f_temp.transpose(1, 2) # [B, M, T, D]
+        output_h_f = input_h_f + self.W_f(h_f_temp) # residual connection, eq. (8)
+
+        return output_h_t, output_h_f, output_h_c
\ No newline at end of file
diff --git a/model/stack_c.py b/model/stack_c.py
new file mode 100644
index 0000000..fe67f23
--- /dev/null
+++ b/model/stack_c.py
@@ -0,0 +1,90 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Attention_module(nn.Module):
+    def __init__(self, hp):
+        super(Attention_module, self).__init__()
+        self.rnn_cell = nn.LSTMCell(input_size=2*hp.num_hidden, hidden_size=hp.num_hidden, batch_first=True)
+        self.num_hidden = hp.num_hidden
+        
+        self.W_g = nn.Linear(hp.num_hidden, 3*hp.M)
+
+        
+    def attention(self, h_i, memory):
+
+        phi_hat = self.W_g(h_i)
+        self.ksi_hat = self.ksi_hat + torch.exp(phi_hat[:, :self.M])
+        self.beta_hat = torch.exp( phi_hat[:, self.M:2*self.M] )
+        self.alpha_hat = F.softmax(phi_hat[:, 2*self.M:3*self.M], dim=-1)
+        
+        self.u = torch.LongTensor( range(memory.size(1)) )
+        self.u_R = self.u + 0.5
+        self.u_L = self.u - 0.5
+        
+        term1 = torch.sum(self.alpha_hat.unsqueeze(-1)*
+                          torch.reciprocal(1 + torch.exp((self.ksi_hat.unsqueeze(-1) - self.u_R) / self.beta_hat.unsqueeze(-1))), dim=1)
+        
+        term2 = torch.sum(self.alpha_hat.unsqueeze(-1)*
+                          torch.reciprocal(1 + torch.exp((self.ksi_hat.unsqueeze(-1) - self.u_L) / self.beta_hat.unsqueeze(-1))), dim=1)
+        
+        weights = (term1-term2).unsqueeze(1)
+        
+        
+        context = torch.bmm(weights, memory)
+        
+        termination = 1 - torch.sum(self.alpha_hat.unsqueeze(-1)*
+                                    torch.reciprocal(1 + torch.exp((self.ksi_hat.unsqueeze(-1) - self.u_R) / self.beta_hat.unsqueeze(-1))), dim=1)
+
+        
+        return context, weights, termination # (B, 1, D), (B, 1, T), (B, 1, T)
+
+    
+    
+    def forward(self, input_h_c, memory, input_lengths):
+        B, T, D = input_h_c.size()
+        
+        context = input_h_c.new_zeros(B, D)
+        h_i, c_i  = input_h_c.new_zeros(B, D), input_h_c.new_zeros(B, D)
+        
+        contexts, weights, terminations = [], [], []
+        for i in range(T):
+            x = torch.cat([input_h_c[:, i], context], dim=-1)
+            h_i, c_i = self.rnn_cell(x, (h_i, c_i))
+            context, weight, termination = self.attention(h_i, memory)
+            
+            contexts.append(context)
+            weights.append(weight)
+            terminations.append(termination)
+            
+        contexts = torch.cat(contexts, dim=1)
+        weights = torch.cat(weights, dim=1)
+        terminations = torch.cat(terminations, dim=1)
+        terminations = torch.gather(terminations, 2, input_lengths.unsqueeze(-1))
+
+        return context, weights, terminations
+
+
+    
+class Stack_C(nn.Module):
+    def __init__(self, hp):
+        super(Stack_C, self).__init__()
+        self.hp = hp
+        self.TTS = hp.TTS
+        
+        if TTS==True:
+            self.c_RNN = nn.LSTM(input_size=hp.num_hidden, hidden_size=hp.num_hidden, batch_first=True)
+            
+        else:
+            self.c_RNN = Attention_module(hp)
+
+    def forward(self, x, memory=None):
+        if memory is None:
+            h_c_temp, _ = self.c_RNN(input_h_c)
+            
+        elif:
+            h_c_temp, _ = self.c_RNN(input_h_c, memory)
+            
+            
+        return h_c_temp
\ No newline at end of file
diff --git a/model/tier.py b/model/tier.py
new file mode 100644
index 0000000..8214c33
--- /dev/null
+++ b/model/tier.py
@@ -0,0 +1,96 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .rnn import DelayedRNN
+
+
+class Tier(nn.Module):
+    def __init__(self, hp, freq, layers, tierN):
+        super(Tier, self).__init__()
+        self.TTS = hp.TTS
+        self.tierN = tierN
+
+        self.W_t_0 = nn.Linear(1, hp.hidden_dim)
+        self.W_f_0 = nn.Linear(1, hp.hidden_dim)
+        self.W_c_0 = nn.Linear(freq, hp.hidden_dim)
+
+        self.layers = nn.ModuleList([
+            DelayedRNN(hp, tierN) for _ in range(layers)
+        ])
+
+        # Gaussian Mixture Model: eq. (2)
+        self.K = hp.K
+        self.pi_softmax = nn.Softmax(dim=3)
+
+        # map output to produce GMM parameter eq. (10)
+        self.W_theta = nn.Linear(hp.hidden_dim, 3*self.K)
+
+        if self.TTS==True:
+            self.TextEncoder = nn.Sequential(nn.Embedding(hp.n_voca, hp.hidden_dim),
+                                             nn.LSTM(input_size=hp.num_hidden, hidden_size=self.num_hidden, batch_first=True))
+        
+        
+    def forward(self, x, text=None):
+        if text is None:
+            memory = None
+        elif:
+            memory = self.TextEncoder(text)
+            
+        if self.tierN == 1:
+            h_t = self.W_t_0(F.pad(x, [1, -1, 0, 0, 0, 0]).unsqueeze(-1))
+            h_f = self.W_f_0(F.pad(x, [0, 0, 1, -1, 0, 0]).unsqueeze(-1))
+            h_c = self.W_c_0(F.pad(x, [1, -1, 0, 0, 0, 0]).transpose(1, 2))
+        else:
+            h_t = self.W_t_0(x.unsqueeze(-1))
+            h_f = self.W_f_0(x.unsqueeze(-1))
+            h_c = self.W_c_0(x.transpose(1, 2))
+
+        # h_t, h_f: [B, M, T, D] / h_c: [B, T, D]
+        for layer in self.layers:
+            h_t, h_f, h_c = layer(h_t, h_f, h_c, memory)
+
+        theta_hat = self.W_theta(h_f)
+
+        mu = torch.sigmoid(theta_hat[:, :, :, :self.K]) # eq. (3)
+        std = torch.exp(theta_hat[:, :, :, self.K:2*self.K]) # eq. (4)
+        pi = self.pi_softmax(theta_hat[:, :, :, 2*self.K:]) # eq. (5)
+
+        return mu, std, pi
+    
+    
+    
+    
+    def sample(self, x, text=None):
+        # x: [1, M, T] / B=1, M=mel, T=time
+        if self.tierN == 1:
+            n_slice = x.size(-1)
+            x_t, x_f = x.clone(), x.clone()
+            
+            for i in range(n_slice):
+                h_t = self.W_t_0(x_t.unsqueeze(-1))
+                h_f = self.W_f_0(x_f.unsqueeze(-1))
+                h_c = self.W_c_0(x_t.transpose(1, 2))
+                
+                for layer in self.layers:
+                    h_t, h_f, h_c = layer(h_t, h_f, h_c)
+                    
+                theta_hat = self.W_theta(h_f)
+
+                mu = torch.sigmoid(theta_hat[:, :, :, :self.K]) # eq. (3)
+                pi = self.pi_softmax(theta_hat[:, :, :, 2*self.K:]) # eq. (5)
+                
+                mu = torch.sum(mu*pi, dim=3)
+                
+                x_t[:,:,i+1] = mu[:,:,i]
+                x_f[:,i+1,:] = mu[:,i,:]
+        
+            return mu
+        
+        else:
+            return self.forward(x)
+        
+        
+        
+        
diff --git a/sample.ipynb b/sample.ipynb
new file mode 100644
index 0000000..b7ddd24
--- /dev/null
+++ b/sample.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "from model.model import MelNet\n",
+    "from model.loss import GMMLoss\n",
+    "from torch.utils.data import DataLoader\n",
+    "from utils.data import MelData, MelCollate\n",
+    "from utils.utils import *\n",
+    "import utils.hparams as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "use_GMM=False\n",
+    "dataset = MelData(hp)\n",
+    "collate_fn = MelCollate(hp)\n",
+    "dataloader = DataLoader(dataset, batch_size = hp.batch_size, shuffle=True, drop_last=True, collate_fn = collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda:0')\n",
+    "model = MelNet(hp).to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)\n",
+    "criterion = GMMLoss() if use_GMM else nn.L1Loss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-b43af4f3e978>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/model.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, n_sec, device)\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mtier1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0mtier2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtier1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mtier1_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtier1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtier2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/tier.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m                     \u001b[0mh_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mtheta_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mW_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_f\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_h_t, input_h_f, input_h_c)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;31m####### time-delayed stack #######\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;31m# Fig. 2(a)-1 can be parallelized by viewing each horizontal line as batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mh_t_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_delay_RNN_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_h_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mh_t_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh_t_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    557\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_packed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward_tensor\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    537\u001b[0m         \u001b[0munsorted_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msorted_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute_hidden\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munsorted_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward_impl\u001b[0;34m(self, input, hx, batch_sizes, max_batch_size, sorted_indices)\u001b[0m\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m             result = _VF.lstm(input, hx, self._get_flat_weights(), self.bias, self.num_layers,\n\u001b[0;32m--> 522\u001b[0;31m                               self.dropout, self.training, self.bidirectional, self.batch_first)\n\u001b[0m\u001b[1;32m    523\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             result = _VF.lstm(input, batch_sizes, hx, self._get_flat_weights(), self.bias,\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    pred = model.sample(1, device=device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Training Start!!!\")\n",
+    "iteration=1\n",
+    "for epoch in range(hp.epochs):\n",
+    "    for tiers in dataloader:\n",
+    "        \n",
+    "        lr_scheduling(optimizer, iteration, 3e-4, 10)\n",
+    "        \n",
+    "        model.zero_grad()\n",
+    "        mu, std, pi = model(tiers[1].to(device), 1)\n",
+    "\n",
+    "        if use_GMM:\n",
+    "            loss = criterion(tiers[1].to(device), mu, std, pi)\n",
+    "        else:\n",
+    "            loss = criterion(tiers[1].to(device), mu[:,:,:,0])\n",
+    "            \n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        iteration += 1\n",
+    "        \n",
+    "        if iteration%100==0:\n",
+    "            fig = plt.figure(figsize=(20,10))\n",
+    "            ax1, ax2, ax3 = fig.add_subplot(221), fig.add_subplot(222), fig.add_subplot(223)\n",
+    "            ax1.imshow(tiers[1][0].numpy(), origin='lower')\n",
+    "            ax2.imshow(mu[0,:,:,0].cpu().detach().numpy(), origin='lower')\n",
+    "            ax3.imshow(np.tile(mu[0,:,:,0][0:1].cpu().detach().numpy(), (16,1)), origin='lower')\n",
+    "            plt.show()\n",
+    "            print(mu[0,:,:,0][0].cpu().detach().numpy()[:10])\n",
+    "            print(mu[0,:,:,0][1].cpu().detach().numpy()[:10])\n",
+    "            print(loss.item())\n",
+    "    break"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:LYH] *",
+   "language": "python",
+   "name": "conda-env-LYH-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tier1.ipynb b/tier1.ipynb
new file mode 100644
index 0000000..11ba63c
--- /dev/null
+++ b/tier1.ipynb
@@ -0,0 +1,849 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "from model.model import MelNet\n",
+    "from model.loss import GMMLoss\n",
+    "from torch.utils.data import DataLoader\n",
+    "from utils.data import MelData, MelCollate\n",
+    "from utils.utils import *\n",
+    "import utils.hparams as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "use_GMM=True\n",
+    "dataset = MelData(hp)\n",
+    "collate_fn = MelCollate(hp)\n",
+    "dataloader = DataLoader(dataset, batch_size = hp.batch_size, shuffle=True, drop_last=True, collate_fn = collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda:0')\n",
+    "model = MelNet(hp).to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)\n",
+    "criterion = GMMLoss() if use_GMM else nn.L1Loss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Start!!!\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00061097 0.00058321 0.00053553 0.0005236  0.0005007  0.00049385\n",
+      " 0.00048758 0.00047873 0.00048196 0.0004847 ]\n",
+      "[3.6096360e-05 3.6650621e-05 3.2866690e-05 3.2536871e-05 3.0277799e-05\n",
+      " 2.9882685e-05 2.9752620e-05 2.8748977e-05 2.9281140e-05 3.3714135e-05]\n",
+      "-6.289918899536133\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00065836 0.00058058 0.00055236 0.00062324 0.00057036 0.00057849\n",
+      " 0.00055954 0.00055323 0.00058215 0.00062819]\n",
+      "[3.4701698e-05 3.2134671e-05 3.5516940e-05 3.8559367e-05 4.0093390e-05\n",
+      " 3.9180803e-05 3.5473142e-05 3.5405144e-05 4.0263127e-05 4.0260707e-05]\n",
+      "-7.500607490539551\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00067367 0.00061139 0.00057499 0.00056138 0.00055463 0.00054977\n",
+      " 0.00054928 0.00057456 0.00060103 0.00056379]\n",
+      "[3.6232588e-05 3.4116965e-05 3.2859640e-05 3.2097643e-05 3.1650699e-05\n",
+      " 3.1443804e-05 3.2347034e-05 3.6994432e-05 3.6429901e-05 3.7004487e-05]\n",
+      "-7.135643005371094\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00068475 0.00063614 0.00059933 0.00058941 0.00057201 0.00056584\n",
+      " 0.00056509 0.00062868 0.00060551 0.00062137]\n",
+      "[3.7821093e-05 3.5635141e-05 3.4620585e-05 3.3297605e-05 3.2406282e-05\n",
+      " 3.2000728e-05 3.5361445e-05 4.4085315e-05 4.5082928e-05 4.1674681e-05]\n",
+      "-7.531097888946533\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00067356 0.00063357 0.0006663  0.00060396 0.00062089 0.00063209\n",
+      " 0.00063352 0.00060474 0.00063634 0.00059183]\n",
+      "[3.7799171e-05 3.9345396e-05 4.0049978e-05 3.6575933e-05 4.0251918e-05\n",
+      " 4.0267623e-05 3.9308532e-05 3.7807033e-05 3.7181315e-05 3.4983699e-05]\n",
+      "-6.486278057098389\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.0006827  0.00060364 0.00057386 0.00056009 0.00055277 0.00054828\n",
+      " 0.00054548 0.00054292 0.00054093 0.00053911]\n",
+      "[3.6694961e-05 3.3805085e-05 3.2769763e-05 3.2341111e-05 3.2148280e-05\n",
+      " 3.2086318e-05 3.2036849e-05 3.2029853e-05 3.2052983e-05 3.2084881e-05]\n",
+      "-7.291070461273193\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00069934 0.00081862 0.00069089 0.00069583 0.00067177 0.00067183\n",
+      " 0.00079976 0.0007844  0.00078175 0.00075146]\n",
+      "[4.4033000e-05 5.2093899e-05 4.8805810e-05 4.5143763e-05 4.6324396e-05\n",
+      " 5.2710900e-05 6.0655744e-05 6.6456501e-05 6.1188250e-05 5.9453876e-05]\n",
+      "-7.864297866821289\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00068563 0.00061821 0.00058509 0.00056989 0.00056415 0.00055532\n",
+      " 0.00055242 0.00055093 0.00069569 0.00072291]\n",
+      "[3.7514004e-05 3.5646390e-05 3.3962675e-05 3.4188877e-05 3.3306940e-05\n",
+      " 3.2969307e-05 3.2887256e-05 3.8489201e-05 4.5900313e-05 5.2858089e-05]\n",
+      "-6.349247932434082\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00070083 0.00062548 0.00059093 0.00076744 0.00073417 0.00071705\n",
+      " 0.00072292 0.00068473 0.0007299  0.00069137]\n",
+      "[3.8302023e-05 3.5992038e-05 3.9804083e-05 5.1863804e-05 5.6232238e-05\n",
+      " 5.8497419e-05 5.6082295e-05 4.5049881e-05 4.7333800e-05 4.3572640e-05]\n",
+      "-8.584881782531738\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00068998 0.00078011 0.00084122 0.00085117 0.00072908 0.00070711\n",
+      " 0.00075504 0.00069793 0.00070086 0.00081165]\n",
+      "[4.0137384e-05 6.5700544e-05 7.0531569e-05 5.6361521e-05 4.5716275e-05\n",
+      " 4.6417659e-05 4.6321260e-05 4.3728825e-05 4.9151866e-05 4.9789116e-05]\n",
+      "-6.378992557525635\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00070889 0.00077019 0.0007375  0.00072313 0.00075233 0.00080643\n",
+      " 0.00076409 0.00074896 0.00086005 0.00074696]\n",
+      "[4.0345723e-05 5.4618595e-05 5.6102297e-05 6.4294836e-05 6.0870720e-05\n",
+      " 5.8467300e-05 4.8817874e-05 5.9832382e-05 5.6364370e-05 5.6666497e-05]\n",
+      "-6.838028907775879\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3+HlHZt7Quwz5iIHNCACAydiDAQAHrN/Ez4ci4pSIOCciNkTE77WemJkXZebazFy7O3b2ORwAAGEPBgBMUV+Jn1LKQ6WU8VLKRET8SUQ0b9JTSrmklLKmlLJmbszvd54AAEPPHgwAmKq+Ej+ZuWqvhz8aEe07HQMAMBD2YADAVO23qldmfjwiXhERR2bm+oh4d0S8IjPPiYgSEfdExNsOZLAcHY3RpUv3iY+MTb1qwc6zT2oeO/7t66vx7WcvbrYpNy+sxhc8tLX+/NNObPa16Lp6xbFm5a5JjJ5WrwIVETF+6x3V+JafOa8aX7BpvNnXUf/7XdX4tp+c22wztuHBarxVuWsyR11fr6q14IpvNtv0U+vi4bPq6zls/SQfhZNOqIYfPbs+g8M/1u7qiVP3ff9HRIyuble2W/D5r7c7bBhZXH+vT2zbNuW+AJgdg9yDzZjJKm4Nss0gx+inetYgK3HNVPWuqZ7nQ3VeEbM/NxXXgGeY/SZ+SikXVMIfPghzAQCgxx4MABiE6VT1AgAAAOAQJvEDAAAA0FESPwAAAAAdJfEDAAAA0FESPwAAAAAdtd+qXoNUxsdj/LHH94kv/sd17UZLllTDjz17XrPJ9u+vl0BfeW29ZHhExHN/84ZqfP32ZdX4zjdsb/Z19689rxqf/+izm21W/tG/1g+Mt0uwP/bz51fjyz761Wp87NUvbPa19RcPrw+/oV7mPSJiZNGianzTj5/dbPPkinpZzGN+5Dv1Mb5Wf/0jIsY3b24ea1l57c5q/IHvn99ss3vJRDW+6up6Kc/NF9Zfl4iIncvr6z/uz25uttnwjpdU4yv/v0nKvE/yvgGAGTfIkumHgqmu51AoWT7IOQ9ybpP1NRPvgZlaJ8AscsUPAAAAQEdJ/AAAAAB0lMQPAAAAQEdJ/AAAAAB0lMQPAAAAQEfNaFWvpkkqEE1sr1fP2vSisWab03/1lmq87NrVbHP739WrPW2+8ORqfMmL2n2tvqI+57z6+mablo2vOKZ57MiPXFuNt+oP3PtL9epUERGn/Gy9etfoiuXNNuOPbqrGl/15vapYRMT4K15Qjc/5RL3iWhkZbDWHjWvq1btO+ky7Qth3Xn9ENb7k1vr6t52ytNnX7sX1j9yWV5/ebLPq4/X3c5x8QrPN+G13No8BwAFpVTuaqUpHrXEOhWpfU51DP5WrBl1taqqvZz9zHnT1ttl+DwxyHBXCgFnkih8AAACAjpL4AQAAAOgoiR8AAACAjpL4AQAAAOgoiR8AAACAjpL4AQAAAOioQ6Kc+yM/dXbz2PI/q5cGX3T33GabiSeemPIcRhYvrsaP/GK9LPaTz1vd7OuRsxZW48fcdlSzzfjDD1fjT5zYbBLLz/2++oFG6ckjv1AvZR4Rcfsfvbgaf9YX2yXg52w7sRqf9/C2Zpudi0brbVolLsfH2+Mfd2w1Pnb/A802J3yyfuy2t61qt/nCk9X4HT9fL/Oez2qvf/SWw6rxI3+8Ped7T6uXen/y6PZrc/rv1ec8tv7+ZhsAOGgO5fLbh0J5+EHp2vqnOrd+StDPlNb4yrwDM8AVPwAAAAAdJfEDAAAA0FESPwAAAAAdJfEDAAAA0FH7Tfxk5qWZuTEzb9ortjwzr8jM23s/63e5BQCgL/ZgAMAgZNnPneQz8+URsTUiPlpKeW4v9rsRsamU8r7MvDgijiil/Kf9DXb46JHlvEVv2HeM49sVlXLL1mr8oTec1Gzz2On1NZ30uZ3NNluetaAaf/i8euWkM957b7Ov2z94dDW+e0e7Etlpv3hdva+PvqDZ5oh/qVfpOvKSeiW00TNObfZV5tfntuH7lzXbrPxv9XHu+PNzmm2WfL1e8WzXkvrzV19Zf/0jIuJrN7SPNTzw6y+pj/PZB5ttbvkv9T31aX9Yr5w1uuGRZl8b//Tw+oHPrGi2KY0iEGOL2tUpRsbqn4GjPlR/zQAG5ZpyZWwpmw7hMkHPHIPcgy3N5eXF+eqpTmDqk55trX3tM3Etz0T9VKia7dcmJ/l38NKuoDowqnoBAzLZHmy/V/yUUq6KiE1PC78pIi7r/X5ZRLx5WjMEAOB72IMBAIPQ7z1+VpZSNkRE9H7WL3EBAGCQ7MEAgCmZc7AHyMyLIuKiiIgFufhgDwcAQDxtDxaLZnk2AMBs6feKn4cyc1VERO/nxtYTSymXlFLWlFLWzMv6fXQAADggfe3B5kb9voAAQPf1m/j5XERc2Pv9woj47GCmAwDAJOzBAIApOZBy7h+PiK9GxHMyc31mvjUi3hcRr8nM2yPiNb3HAAAMiD0YADAI+73HTynlgsahKdYE/Z8d7hPa+LKjmk9f8eE7qvFHz13dbHPav712ytNavqj+3feR8bPqDRa0L5k+5X1j9QN33t1s0yoWmY/Oa7ZZcl9jnIbtJ7dLs8//2/o5O+qwdmn2h99+XjV+2u9ubrYZa1QzH/3qjdX4yMknNPsabx5pW7FudzV+67sbE4uIEz9aLzM6dnj9PfDwi05u9nXUf91SjW/9nYeabR64o/75KAvbZ+CM99dfg37OGQCzY+B7sGEw26XBGaxWqfVBllmfiZLtALOs3696AQAAAHCIk/gBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICO2m9Vr0EqExMxsX37PvEl6+uVliIi5px8YjW+6K65zTY5t14Jq+ze1Z7cyc+qhhc+Uq+cNb7ssGZXW0+pH1sycVx7/Btuqcf3LYL23TksrOfttvxMvdrW6f/+5mZfNx5zfjW+9L72a7N7cb1yxsjWJ5ttRhrr3PZjL67GFzzafs1GbmsearfZWa/csPLz7SptJ/3WTdX4uj94br2vr2xs9rXz/62fz41r2++NZ/1z/T246fR2xbdHzj+6Gj/itjubbQBg2vqpwjQTlZtaY/Q7zmT9TXWMmVj/TI3TT1W1Qb82gxy/5ZlYCUzFu7ZK1WnoGlf8AAAAAHSUxA8AAABAR0n8AAAAAHSUxA8AAABAR0n8AAAAAHSUxA8AAABAR81oOfeWRbe2y1/Hkzur4WP/ed+y8E+557++sBqfs71dxvD0H67XBl8cj1TjO96ysNnXA7+wpBofXXNEs83JN9TjE4va5SIX/coD1fht31lZjR/+jhOafW35iXr8sAfb52zryfUy47f+u1XNNq9/5f3V+D/cs60aP+EtdzT76qeQ5tL/ur4a/876Y5ttnvzI86rx3c+qn5sHX3Zks6/DvjBajS9qV62PDT9f/wws/nL743vUld+pxuuvGABDa6ZKPPdTsnumypz3Y5BzmKn1TPU899NXH3Kk/R4sE1Oc22Tz6med/fQ11XMz2WewnzLjyrZPnXPWptR9Z7jiBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjZrSqV86bG3OOXb1P/NGXtisqLbn3yWp8/nsfarYZufKkanzHc3c029z9sVOr8c3fV78z/2kPfLPZ14KN+64xIiInucn/Yz93fjV+5DXtNju+UD9vp33269X4rteuafZ10sVfrcZHzjq92Wbh/cvr8Yfbd3//53tfVI0vGKu3ufWSM5t9nfrz32gea7n3k6dU46/9hXZfJ539cDX+kb/8wWr88G/XK3dFRMx5fb1K3NtPuarZ5sPveXM1/uTPbmq22X53vbLavPX1qmoADIFDtXLNVKt39VNRadCVsw7VimOHwrmZojIxwKpBg6zcNZl+qocN8jwfqp9lusd77Zllkr9OXfEDAAAA0FESPwAAAAAdJfEDAAAA0FESPwAAAAAdNa2bO2fmPRHxRESMR8RYKaV992AAAAbCHgwAOFCDqOr1ylJKvVTR04wdNi82veS4feJL72pX28qvfqsav/Ph5zXbrLxudzW+ecvCZpuJRiGmY66u38l8++vPafa16MH67bRXfLheOSsiYuxVL6zGH3zx/Gab7cfUX77jP1t//qbnzGv2ddTO51fjow883mxzzLW7qvEdK9pvq/mPj1Xjc3aMV+Mr1g2w0kNEnHthvRrbTe89q9nmn86sr+eoVz5Qjb/kqLuafV1+x9nV+Ed+403NNhOL6vE5n6lXVYuI2HlE/by13wEAPAMd8B6sqZ8qSJNUJ8qR+r6pr8pN/VRHGvB6pmyQVaUGvc6ptpmhal+t98yeKQx2HzjV8dvaFVzLeH1P2xcVlYAB8VUvAAAAgI6abuKnRMSXMvO6zLxoEBMCAGC/7MEAgAMy3a96vbSU8kBmHh0RV2TmLaWUq/Z+Qm8zclFExLxFR0xzOAAAYop7sAXR+N4wANB507rip5TyQO/nxoi4PCLOrTznklLKmlLKmjkLFk9nOAAAYup7sLnRvmcgANBtfSd+MnNxZi556veIeG1E3DSoiQEAsC97MABgKqbzVa+VEXF57rnb/JyI+MtSyhcHMisAAFrswQCAA9Z34qeUcldE1GtTN+REibk79i0N+Usf+VyzzR/+5k9V48sub49z/ysapQ8nKQm5tFGB+4nV9YuiVv/Zbc2+HvwPz6nGl73yBc028/715mp8+0+1y9YvunduNT76nGdX48+94NvNvu6757Rq/PETVzbbPHZ6PT52WLv850l/Uz/22Cn1S9CP/sqDzb7Kovr9Cia2b2+2ufL2+qT/xx/+QbPNj33k16vxJz69qhr/+m3tMutLV9fX+Ue/3x7/0UY993t2HdVs8+k3v6waH2CBUQBmST97sH60S7NP/YLxSUtmT7lse7uUdj/6Ws9oYw6NOU9WlnzGzvMU9TN+PyXo+zk3gzTokvGt90ZznMnOGcCAKOcOAAAA0FESPwAAAAAdJfEDAAAA0FESPwAAAAAdJfEDAAAA0FHTKec+ZePzMzafuu+Qv/WxC5ptlsyp3wH/kee378D/9td8qRr/63vbVbWueMtHq/Fv7lxcjb/t8Lc1+/qHn/m/q/HfftVrmm1ufn+9OMd7X/GpZpufXfJoNf7WN9YrOv3jjWc0+/qdD/x1Nf57t7fnvPv+w6vxD7zyr5pt3vhjm6vxL+2on+d3vOjnmn2d+Tv1GlUT93yn2WZivF4d4t+/5X9tthl/Q/299uJf/mY1fu6SRom4iPjdm15bjf+Xl/94s83Ws4+txjed3v74jv/2lmp8xV+8uBpfdPk1zb4A6IhKVaV+qib1VaFrMq2qRlOu9tWfdlWtSao9TXEOAz/PDf1UqGqN06xcFhFlfHB1Qmeicle/47fO52zP+ZDWz98BM0H1NIbcIfrJBAAAAGC6JH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOipLmXrZx37NP+n4suo337HvgZH2HFZ8ZX41/vb/cHmzzf/1xTdX46VRGj4iYtH99ZKVq15zX72v9x7V7OvOn6qX2V79hWaT2HxavU1pV9KMHKvHj/3Av1bjD/z6S5p9HX53vSznptPbEzj8rnpZxOX/XD9nkxk7fkU1/vBv7Gy2Wfl/1Oc2cf23m23ecHO9nPylH/rhZpvD795djS+8f1s1vmNVvTR9RMSOI+uv8/wn2mVRD7u1Puf7fqT9Hjz+7+ttRrZsr8bH7r632RfAVFxTrowtZZNax4eYpSPLy3lzfnCfeM6p/3cpIiJG6v8+WHY3NiAREf2UuW6VIB9kX5OZajn5fvRTSnrQZbGnOod+xu9jnZO+B1sa781JTdTnNun/C/Xx3sysH2uOM97eA7bKyU9GqXkYXl8b+/vYMlHfg7niBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCj+riN/jTszhh9cN4+4ZGT6tWRIiJW/0K9QtTH/93rm22es/6hanzrc9tVkJZce081/vgdq6vxw++tjxERccw/HVONL/zs19rjn1AfZ/2P1uMREcd+uV65qVVPYdXV7fOcX/1WNb7o/LObbebeUz8H48fWK3RFROQt91Tjo7vrlbOO/j9XNvuauP7G5rGW//7n9epdE0e025zylnXV+L89+p+q8eWjTzb7+sr2U6vx9//9G5ttVs6vn89tZ7Yrnv3yL/9tNf7um36kGj/ux5pdAdABOTIaI4dVqk5OVlGpUdVp0ppBrapO431UtRpt/PvkZNWmWuNPVrlpkqpKTaONqqetKpFK0xwAABnmSURBVEyTjdFPFabW+P2spR+t16Cf8ef28b8jjcpZk7fp4/080VjPZNW2Gq9nc5zJqnr187kBhlZubf/30RU/AAAAAB0l8QMAAADQURI/AAAAAB0l8QMAAADQUdNK/GTm6zLz1sy8IzMvHtSkAABoswcDAA5U34mfzByNiD+OiB+KiDMj4oLMPHNQEwMAYF/2YADAVEynnPu5EXFHKeWuiIjM/EREvCkivt1qMLI7YtGD+xYzPOqL7Wls31ov573ttH3Lwj9lzuOLqvFFn/9Gs83Y2Fh9nJUn1p9/fr1ke0TEtlX1fNrSZouIsXvrZeuX3bmq2WbiW/Uy46Mrltcb3PFAs69WIcldy9vn+cmjn1WNL/zs15ttmsUvtzVKzZ/QLudeXnpONZ5XX99sc+SN9bLx8//22mabB/+8vs7f2vTKanx8y5ZmX6NnnlaNn77p7mab0ih1v/TT7XEuXV2f23F339xsA8AzxpT3YAtPG4/v+8t9/7vx3pXt/2bfO7arGv/XHSc12/zgorsafS1strlz99HNYzU/cdiDzWNf3nFYNf66RTubbf74sdXV+DWPt9f5q8d8oRq/fme9r+u31fcSERGnL9xQjW/YvazZ5owF9T3dktEdzTa3PnlsNf7kxNxqfO5IfW8cEXHs3Meq8cnWeeNj9fHfd9Jnmm2+/mT9NVg8Un89t03Mb/a1u4xW4ytGtzbbPDZe/3+K7ZOMM5L1EuxbxxdU41/b1H6fbdlZbzNR2kXoR3KSUvNTNMi+Zspk52a2HcpzG6Rn3rtmZszE6z/2v9X/Po+Y3le9jouIvbMV63sxAAAOHnswAOCATSfxU0tZ7ZPgy8yLMnNtZq4d39G4qgMAgAM15T3Y9sfaV7wAAN02ncTP+ojY+3rW4yNin+tOSymXlFLWlFLWjC5cPI3hAACIPvZgi5a1v5oCAHTbdBI/10bEqZl5UmbOi4ifjojPDWZaAAA02IMBAAes75s7l1LGMvMdEfH3ETEaEZeWUtw5FgDgILIHAwCmIkuZuftuZ+bDEXFv7+GREfHIjA1+6Bn29Uc4B9Zv/dY/3Lp6Dk4opRw125Pge9mDfQ/rH+71RzgH1m/91t9NzT3YjCZ+vmfgzLWllDWzMvghYNjXH+EcWL/1W//wrj/COWD2DPt7z/qHe/0RzoH1W7/1D9/6p3OPHwAAAAAOYRI/AAAAAB01m4mfS2Zx7EPBsK8/wjmw/uFm/TgHzJZhf+9ZP8N+Dqx/uFn/EJq1e/wAAAAAcHD5qhcAAABAR81K4iczX5eZt2bmHZl58WzMYSZl5qWZuTEzb9ortjwzr8jM23s/j5jNOR5Mmbk6M7+cmesy8+bMfGcvPhTnIDMXZObXM/NbvfX/Zi9+UmZe01v/X2XmvNme68GUmaOZ+c3M/Hzv8bCt/57MvDEzr8/Mtb3YUHwGIiIyc1lmfiozb+n9XXD+sKw/M5/Te92f+rMlM981LOvn0DFs+68IezB7MHuwiOHegw37/ivCHswebI8ZT/xk5mhE/HFE/FBEnBkRF2TmmTM9jxn2kYh43dNiF0fElaWUUyPiyt7jrhqLiF8rpZwREedFxK/0XvNhOQc7I+JVpZSzI+KciHhdZp4XEe+PiA/21r85It46i3OcCe+MiHV7PR629UdEvLKUcs5eJSSH5TMQEfGHEfHFUsrpEXF27HkvDMX6Sym39l73cyLihRGxPSIujyFZP4eGId1/RdiD2YPZg0XYgw3z/ivCHsweLGbnip9zI+KOUspdpZRdEfGJiHjTLMxjxpRSroqITU8LvykiLuv9fllEvHlGJzWDSikbSinf6P3+ROz5y+a4GJJzUPbY2ns4t/enRMSrIuJTvXhn1x8RkZnHR8QPR8Sf9h5nDNH6JzEUn4HMXBoRL4+ID0dElFJ2lVIeiyFZ/9O8OiLuLKXcG8O5fmbP0O2/IuzB7MHswezBqobi/R9hD/Y0Q70Hm43Ez3ERcd9ej9f3YsNmZSllQ8Se/yhHxNGzPJ8ZkZknRsTzI+KaGKJz0LvE9vqI2BgRV0TEnRHxWCllrPeUrn8O/iAi/mNETPQer4jhWn/Eno3mlzLzusy8qBcbls/AyRHxcET8We9S8z/NzMUxPOvf209HxMd7vw/j+pk99l/fNZSfPXswe7De42Hbgw3z/ivCHmxvQ70Hm43ET1ZiSosNgcw8LCI+HRHvKqVsme35zKRSynjvEsPjY8+/up5Re9rMzmpmZOYbImJjKeW6vcOVp3Zy/Xt5aSnlBbHnaxa/kpkvn+0JzaA5EfGCiPhQKeX5EbEthuCS2qfr3UPhjRHx17M9F4bSMP69S489mD3Y3uHKUzu5/p5h3n9F2INFhD1YxOwkftZHxOq9Hh8fEQ/Mwjxm20OZuSoiovdz4yzP56DKzLmxZ8PxF6WUz/TCQ3UOIiJ6l1Z+JfZ8z35ZZs7pHery5+ClEfHGzLwn9ny14FWx51+fhmX9ERFRSnmg93Nj7Plu8bkxPJ+B9RGxvpRyTe/xp2LPJmRY1v+UH4qIb5RSHuo9Hrb1M7vsv75rqD579mB72IMN5x5syPdfEfZgTxn6PdhsJH6ujYhTe3eTnxd7Lrn63CzMY7Z9LiIu7P1+YUR8dhbnclD1vkv84YhYV0r5/b0ODcU5yMyjMnNZ7/eFEfEDsec79l+OiJ/oPa2z6y+l/OdSyvGllBNjz+f9H0spPxtDsv6IiMxcnJlLnvo9Il4bETfFkHwGSikPRsR9mfmcXujVEfHtGJL17+WC+O4lxhHDt35ml/3Xdw3NZ88ezB5smPdgw77/irAH28vQ78GylJm/si8zXx97ss2jEXFpKeW3Z3wSMygzPx4Rr4iIIyPioYh4d0T8TUR8MiKeFRHfiYifLKU8/eaDnZCZL4uIf46IG+O73y/+jdjzHfPOn4PMPCv23DRsNPYkWz9ZSvmtzDw59vzry/KI+GZEvKWUsnP2ZnrwZeYrIuLXSylvGKb199Z6ee/hnIj4y1LKb2fmihiCz0BERGaeE3tuLDkvIu6KiF+M3uchhmP9i2LP/VVOLqU83osNzevPoWHY9l8R9mD2YPZgTxnGPZj91x72YPZgEbOU+AEAAADg4JuNr3oBAAAAMAMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKMkfgAAAAA6SuIHAAAAoKP2m/jJzEszc2Nm3rRX7D2ZeX9mXt/78/qDO00AAAAApipLKZM/IfPlEbE1Ij5aSnluL/aeiNhaSvnAVAabs3Bxmbdk+T7xZx/zYLPNo+OLq/FHth3WbLN6yaZ6X7vbbbbvmlc/0Dg9py95qNnXHTuOrMbPWLS52ebGzUdV4/O2tF+fhcfuqMYf376wGh/dls2+xutNYmR3s0lMLJyojzNaj0dEjO+YU41na5ntKUeZ1xh/azufOXdLfUHzTm4vdMu2xskZbUx6fLJJN47NaZ+zGKuvJydp0jydjTbzN4+1O9vVODeT/d2Rk5yDqeqrq34aTf534Wx1NXD7+Tu/2uQgTGMwZmhmh+4JOCQ9GdtiV9k5wL8EAACYjvr/he+llHJVZp44iMHmLVkep/6bX90n/j8u/t1mm48+9sJq/E++/v3NNr/7v3yiGv/Yg+c123zj7mfVDzQ2/H/3yv+n2dcbb/ilavzrL/hks80pn3x7Nb76ivFmm+e+54Zq/G+/eVY1vuLa9su96ax6RmDR/aPNNk+etb0aX3pYPSEVEfHEt/dN/EVEzHmy/v8IE3Pb/8c19qwn6+N/rZGoiYhVX6onGVd/dEOzzZe+8bxqfGRxPSEy8cTcZl+5u5HEWb6z2aY8Vk9KztnWTnC18ktzt9QPnPg3j7b7uvu++oGJSTJPI425TZYQarTJfpJII320mRjc/903E+qTnbN2Z9ObzADmsL9/IJja+INMsPVxPvsaZqYSTANcz6DfN1NwTbly1sYGAGBf07nHzzsy84beV8GOGNiMAAAAABiIfhM/H4qIUyLinIjYEBG/13piZl6UmWszc+3Yjm19DgcAAADAVPWV+CmlPFRKGS+lTETEn0TEuZM895JSyppSypo5C+v36wEAAABg8PpK/GTmqr0e/mhE3NR6LgAAAACzY783d87Mj0fEKyLiyMxcHxHvjohXZOY5sefWx/dExNsO4hwBAAAA6MOBVPW6oBL+8EGYCwAAAAADNJ2qXgAAAAAcwiR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgoyR+AAAAADpK4gcAAACgo/ab+MnMSzNzY2betFdseWZekZm3934ecXCnCQAAAMBUHcgVPx+JiNc9LXZxRFxZSjk1Iq7sPQYAAADgELLfxE8p5aqI2PS08Jsi4rLe75dFxJsHPC8AAAAApqnfe/ysLKVsiIjo/Tx6cFMCAAAAYBAO+s2dM/OizFybmWvHdmw72MMBAAAA0NNv4uehzFwVEdH7ubH1xFLKJaWUNaWUNXMWLu5zOAAAAACmqt/Ez+ci4sLe7xdGxGcHMx0AAAAABuVAyrl/PCK+GhHPycz1mfnWiHhfRLwmM2+PiNf0HgMAAABwCJmzvyeUUi5oHHr1gOcCAAAAwAAd9Js7AwAAADA7JH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOkriBwAAAKCjJH4AAAAAOmrOdBpn5j0R8UREjEfEWCllzSAmBQAAAMD0TSvx0/PKUsojA+gHAAAAgAHyVS8AAACAjppu4qdExJcy87rMvKj2hMy8KDPXZubasR3bpjkcAAAAAAdqul/1emkp5YHMPDoirsjMW0opV+39hFLKJRFxSUTEoqNXl2mOBwAAAMABmtYVP6WUB3o/N0bE5RFx7iAmBQAAAMD09Z34yczFmbnkqd8j4rURcdOgJgYAAADA9Eznq14rI+LyzHyqn78spXxxILMCAAAAYNr6TvyUUu6KiLMHOBcAAAAABkg5dwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOkvgBAAAA6CiJHwAAAICOmlbiJzNfl5m3ZuYdmXnxoCYFAAAAwPT1nfjJzNGI+OOI+KGIODMiLsjMMwc1MQAAAACmZzpX/JwbEXeUUu4qpeyKiE9ExJsGMy0AAAAApms6iZ/jIuK+vR6v78UAAAAAOARMJ/GTlVjZ50mZF2Xm2sxcO7Zj2zSGAwAAAGAqppP4WR8Rq/d6fHxEPPD0J5VSLimlrCmlrJmzcPE0hgMAAABgKqaT+Lk2Ik7NzJMyc15E/HREfG4w0wIAAABgurKUfb6ddeCNM18fEX8QEaMRcWkp5bf38/yHI+Le3sMjI+KRvgd/5hv29Uc4B9Zv/dY/3Lp6Dk4opRw125MAAGCPaSV+pjVw5tpSyppZGfwQMOzrj3AOrN/6rX941x/hHAAAMDOm81UvAAAAAA5hEj8AAAAAHTWbiZ9LZnHsQ8Gwrz/CObD+4Wb9OAcAABx0s3aPHwAAAAAOLl/1AgAAAOioWUn8ZObrMvPWzLwjMy+ejTnMpMy8NDM3ZuZNe8WWZ+YVmXl77+cRsznHgykzV2fmlzNzXWbenJnv7MWH4hxk5oLM/Hpmfqu3/t/sxU/KzGt66/+rzJw323M9mDJzNDO/mZmf7z0etvXfk5k3Zub1mbm2FxuKz0BERGYuy8xPZeYtvb8Lzh+W9Wfmc3qv+1N/tmTmu4Zl/QAAzK4ZT/xk5mhE/HFE/FBEnBkRF2TmmTM9jxn2kYh43dNiF0fElaWUUyPiyt7jrhqLiF8rpZwREedFxK/0XvNhOQc7I+JVpZSzI+KciHhdZp4XEe+PiA/21r85It46i3OcCe+MiHV7PR629UdEvLKUcs5eJbyH5TMQEfGHEfHFUsrpEXF27HkvDMX6Sym39l73cyLihRGxPSIujyFZPwAAs2s2rvg5NyLuKKXcVUrZFRGfiIg3zcI8Zkwp5aqI2PS08Jsi4rLe75dFxJtndFIzqJSyoZTyjd7vT8Se/+E7LobkHJQ9tvYezu39KRHxqoj4VC/e2fVHRGTm8RHxwxHxp73HGUO0/kkMxWcgM5dGxMsj4sMREaWUXaWUx2JI1v80r46IO0sp98Zwrh8AgBk2G4mf4yLivr0er+/Fhs3KUsqGiD2JkYg4epbnMyMy88SIeH5EXBNDdA56X3O6PiI2RsQVEXFnRDxWShnrPaXrn4M/iIj/GBETvccrYrjWH7En2felzLwuMy/qxYblM3ByRDwcEX/W+7rfn2bm4hie9e/tpyPi473fh3H9AADMsNlI/GQlprTYEMjMwyLi0xHxrlLKltmez0wqpYz3vuZxfOy56u2M2tNmdlYzIzPfEBEbSynX7R2uPLWT69/LS0spL4g9X3P9lcx8+WxPaAbNiYgXRMSHSinPj4htMYRfa+rdx+qNEfHXsz0XAACGx2wkftZHxOq9Hh8fEQ/Mwjxm20OZuSoiovdz4yzP56DKzLmxJ+nzF6WU/7+9u1etKojCMPwuEgIqQvCnE5GA2ImVjRZBRVDESgtRCN6DjTaCkNY70FIhiD+5ABWsRNBC0U5Egxg7S6vPYiYk2Ccb9n6f5szMPsWsOUyzzszaT/vwpNYAoF9veU2rdTRfVbP90Zj3wSngclV9o13tPEM7ATSV+AFI8rN//qbVdznJdPbAGrCW5G3vP6ElgqYS/4YLwPsk670/tfglSZI0gCESP++Ao/2NPnO0Y++rA8xjaKvAUm8vAS8GnMu26vVcHgBfktzf8mgSa1BVB6tqvrd3AedodY5eAVf610Ybf5LbSQ4lOULb7y+TXGci8QNU1Z6q2rvRBs4Dn5jIHkjyC/hRVcf60FngMxOJf4trbF7zgunFL0mSpAFUsvO3K6rqIu0f/xngYZLlHZ/EDqqqx8AicABYB+4Cz4EV4DDwHbia5P8C0KNQVaeBN8BHNmu83KHV+Rn9GlTVcVrh1hlasnUlyb2qWqCdgNkHfABuJPk73Ey3X1UtAreSXJpS/D3WZ707CzxKslxV+5nAHgCoqhO04t5zwFfgJn0/MI34d9Pq2y0k+dPHJvP7S5IkaTiDJH4kSZIkSZK0/Ya46iVJkiRJkqQdYOJHkiRJkiRppEz8SJIkSZIkjZSJH0mSJEmSpJEy8SNJkiRJkjRSJn4kSZIkSZJGysSPJEmSJEnSSJn4kSRJkiRJGql/R2SM9fSdgPkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00070508 0.0008817  0.0008411  0.00076463 0.00085867 0.00081527\n",
+      " 0.00077252 0.00077099 0.00088302 0.00079468]\n",
+      "[4.5048931e-05 5.7672711e-05 6.0207974e-05 6.0532609e-05 5.6772460e-05\n",
+      " 5.0004099e-05 4.9238941e-05 5.4903412e-05 6.1231382e-05 5.4592969e-05]\n",
+      "-7.183434963226318\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00074639 0.00066055 0.00063002 0.00061684 0.00061017 0.00060643\n",
+      " 0.00060458 0.00060302 0.00060429 0.00060659]\n",
+      "[4.2023523e-05 3.9021099e-05 3.7992155e-05 3.7567741e-05 3.7356607e-05\n",
+      " 3.7237383e-05 3.7193870e-05 3.7133374e-05 3.7269921e-05 3.7466456e-05]\n",
+      "-7.087035179138184\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00073214 0.00066898 0.00073652 0.0009015  0.00094958 0.00096001\n",
+      " 0.00087161 0.00084891 0.00084002 0.00079268]\n",
+      "[4.2724525e-05 4.0089526e-05 5.1264004e-05 7.6759534e-05 8.3990912e-05\n",
+      " 7.5753924e-05 6.0946801e-05 5.8556237e-05 6.6348919e-05 6.9086520e-05]\n",
+      "-8.335579872131348\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00074314 0.00101307 0.0009037  0.0009178  0.00090932 0.00089806\n",
+      " 0.00080637 0.00093344 0.00069325 0.00112598]\n",
+      "[4.7995880e-05 6.5615641e-05 6.5665219e-05 6.7134104e-05 6.2583611e-05\n",
+      " 5.7304693e-05 5.7041787e-05 6.0177197e-05 5.8511803e-05 6.9326081e-05]\n",
+      "-7.086644649505615\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00076603 0.00098492 0.00091564 0.00089071 0.00095915 0.00086999\n",
+      " 0.00094076 0.00111039 0.00089841 0.00090638]\n",
+      "[4.7810092e-05 7.7113080e-05 7.2473027e-05 7.7052806e-05 7.0668371e-05\n",
+      " 6.4168344e-05 8.1604179e-05 7.1724302e-05 5.9800608e-05 6.6361892e-05]\n",
+      "-8.416759490966797\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00074963 0.00086709 0.00096538 0.00091211 0.00111171 0.00102263\n",
+      " 0.00106566 0.00090271 0.00092942 0.00090264]\n",
+      "[4.2839656e-05 6.8590824e-05 7.5729440e-05 7.4834876e-05 8.4432184e-05\n",
+      " 8.8753630e-05 8.5231251e-05 7.2367846e-05 6.9587637e-05 6.5076805e-05]\n",
+      "-7.182511806488037\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00078509 0.00085551 0.000989   0.00095244 0.00096967 0.00073193\n",
+      " 0.00093142 0.00104239 0.00105564 0.00080977]\n",
+      "[4.5068613e-05 7.2050549e-05 7.9064717e-05 7.1954892e-05 6.7749119e-05\n",
+      " 5.9778773e-05 6.7072287e-05 7.5819909e-05 7.7497010e-05 6.3461463e-05]\n",
+      "-6.688114166259766\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.000794   0.00119099 0.00114036 0.00093709 0.00130315 0.00121168\n",
+      " 0.00108675 0.0011376  0.00099635 0.00103472]\n",
+      "[5.7085541e-05 8.0991806e-05 7.5828728e-05 7.6961231e-05 9.5839583e-05\n",
+      " 8.1960890e-05 8.1366328e-05 7.0268943e-05 7.0406917e-05 6.7696164e-05]\n",
+      "-7.035499095916748\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00080206 0.00108934 0.00098034 0.00123307 0.0009525  0.0012469\n",
+      " 0.00108251 0.00105296 0.00104954 0.00117047]\n",
+      "[4.8647686e-05 8.6161788e-05 9.7030519e-05 1.0547773e-04 7.6311189e-05\n",
+      " 8.8887537e-05 7.9907892e-05 7.3644696e-05 7.8398698e-05 8.2310136e-05]\n",
+      "-7.333347320556641\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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PxsrbHmvm+05f2ckW7jzYHDu+pv36m9zevjYAMA3HetcsnboAOEq84wcAAACgpxR+AAAAAHpK4QcAAACgpxR+AAAAAHrqsIWfUso7SylbSik3PS57cynl/lLK1wb/vWxupwkAcGKxBwMARmE6Xb3eleQ/JXnP9+Vvq7X+zigmMbntkW64oN29a+eZ7VrVzjc+r5mf9rvdbl+n3dCex74fvbyZr7h5Syd77FkbmmP/j5d+pJn/+4/9WHcep7Q7WOXRmXX7OusjD3Wyu3/81ObYtbe1+6Ut/bUVnWzv2Uva9/c3B5r53vVDun1Nde9zx3lLm0Mfvbidn/8by7qX3dPu0rXnye2uXkvv39XMt13S7V6249n7m2Of/PvtNT5yUTdf/Fj7x2vtF3TvAmBa3pU53oMdM471jlxzqbX2ma7xRH78YD74GeM4d9h3/NRaP5ekUZkBAGCu2IMBAKMwmzN+fq6U8o3B25DXjmxGAAA8EXswAGDajrTw8wdJzk9yWZIHk/zusIGllKtLKZtKKZsOpv3xGQAApsUeDACYkSMq/NRaN9daJ2utU0n+KEn7cJxDY6+ptW6stW5cmO5ZKgAATI89GAAwU9M53LmjlHJarfXBwc0fS3LTE40/Io0DgZNkfE/7sKwd57cvM/GiZ3cvvahd77rnqqn2RX50fSdadlf78Ol/999e2czHz97dyfaeu6Y5dtG32tMYZvK2OzrZkkfah08v3do+mHnfKd1DlZffurU59tZ/035XednWnt/qDyzqXvuB9jzWXftgM58YcpBzy6NPbh/AvOPs9uO965zu875ga3fOSbJlYzvfcVn3X1KX3NXeZK9b3309Jcnk1vbjDQDfNS97sKNhvg9DHXZQ69E4lHUU9znsGjM5kHYuH5Ojce25ur+jYSZr5OjzfHG0PMGvuMMWfkop703ygiTrSin3Jfn1JC8opVw2uPRdSV4/gmkCADBgDwYAjMJhCz+11tc04nfMwVwAABiwBwMARmE2Xb0AAAAAOIYp/AAAAAD0lMIPAAAAQE8dUVevI7Z8afKMZ3Tisumbnaw+66nNS0wubp+SvubW9hHWCw52OzYt+sq3m2PP339hM1989yOdbP85JzXHPvbGXc38wrXdjk23nf6U5tj2lYfb/ePP6WQbPt9usXXwpGXNfMnHvtwNN5zSHPvUN7cf67pjZzMvG7pdrOpY+3ncfdkZzXzx/Q8085aDy9v5yis3N/NVv3dyJ9t6absz2Kp7Djbzxa/Y3sl2rWl39Srj7Y5w+36025F3xU3tOU/ceXczB4B5N98dbGbamak0/p2zDunkOgoz7WDVGj/jNY7gOZjLjldzufaZOJa6ubXoBgXMEe/4AQAAAOgphR8AAACAnlL4AQAAAOgphR8AAACAnlL4AQAAAOipee3qNbVwQfacvrSTj/3wMzvZ4o9/pXmNdSuf3czv/Kn2fW5/UTdb+cWnN8euvqrdOeqOW07tZBuua9/fsj9c08yX/9r9nayO6NFfdWO3g9fOi7udqpJk5WfaHc12NzpKLf9Ot1NVktzzsnXNfHxPe34brnusky3+zI3twcOMdx+sOjHRHHrSrZPNfNWH2w/49md0u2ydckO7e9eiT7Rfl/f/o8u64b3d13qSnPmRO5v5nb90oBseaM8DAE5Yo+h81Or0lSQZ0u1rFF2fjvWOTSdCx6tjpbsYwDzzjh8AAACAnlL4AQAAAOgphR8AAACAnlL4AQAAAOipeT3ceWznvqz821s7+eT27uG/wzx64aJmfvrH2ofx7T6le3Dv6R/6TnPs5JdPaeZPueeOTjZx3mnNsWv+w33NfPn4/k528qvvbY6tN1/azMsXv97Mtz5vfSdb0D7fOFPnnt7Ml/zVl7vhRRc0x571jlvaF69DDkQ8o3s49tTT2tfet2FZMx922HfLqk+151dWrWzmD71sRSd7yr9tvyZ3XdU9BDtJFt3U/VHat779eNxz9ZOa+WNP7x4GvXTVGc2xi+9vH0QOAL3SOmD3aBySPN8HHx9LBy2P4uDjmaxnLp/fmVz7WHoOAGbJO34AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAeuqwXb1KKe9M8vIkW2qtTxtkJyV5f5Jzk9yV5FW11kcPe29jC1JWr+rmra5eC7rduJLk1M8/0szrPe0uRwte/NRONvHQ5vbYdWub+VRjfnvOaHdmeujDFzXze168pZNtXN/u6vXXrzirmZ/3xWacdddv72RTX293tpp4wbOa+fh496VQDk40x9ZFC5v51Fntrmh1002dbGxV43WQZNmeDc28nNbtDDbx4EPNsXf8i0ua+brL28/7hf/mYCe7/S3dTl9JMja+t5nvf6jbjexJT32wOfah553ZzLc/+0AnO/O/tn8OAOi/ke7BjldHo4PXsWwUj8dcXmNU3b6OZbp9Aceh6bzj511Jrvy+7E1Jrq21Xpjk2sFtAABG512xBwMAZumwhZ9a6+eSfP/bbK5K8u7B1+9O8soRzwsA4IRmDwYAjMKRnvGzodb6YJIM/mx/xidJKeXqUsqmUsqmA5Ptj8kAADAtR7QHO5j98zZBAODYMueHO9dar6m1bqy1blw0tnSu7w4AgHzvHmxhFh/t6QAAR8mRFn42l1JOS5LBn92TiwEAGDV7MABgRg7b1WuIjyZ5bZLfGvz5kel80+TyRdn+nDM6+dhlp3WypR/5cvMae89qd4Pa8hvtpSz5625XpOWXdjt9JclDf7/d1Wvhs7v5Yxc2h6aOt0/0f9GGuzrZpq1nN8ee/8Fd7Wu37zLlQLf71m2/95zm2JNvaNf6Trm7+7xMrl7eHHvO+9rdsT759XOa+cVbu13Ktr6w3dlqzW3tjwOWW25r5i2nf67bHStJFv3GPc38/r94cidb+tmVzbFnfPLhZn7vb052skc+3F7j2tuHzG9Xt1vawaU6RADwPY5oDzYjx2vXojKCN7IPu0admv21T2QzeVxn+jzO1TUAeuSwvxVLKe9N8qUkF5VS7iulvC6HNhsvKaXcluQlg9sAAIyIPRgAMAqHfcdPrfU1Q/7qxSOeCwAAA/ZgAMAozPnhzgAAAAAcHQo/AAAAAD2l8AMAAADQU0fa1euIjO3Ym9Wf+GYnn9q7r5M99AvPa15j8fZ2R4lzfu1gM9+2sZsdXLu0OXb99bubefni17vX+Nn2/H7qn3+imbd8/PofaOYP/Pz+Zn7hP25f585Xre9kT33b/c2xddmSZj5x592dbN+PXt4ce99PrGvmF5zVfg4m7r63MZF2x6vdpy9u5iuaadud/6DbyS1JnvOWdke4nW/v5g++vPuaTJIl/+ujzXz128/rZC/+N59tjv3vDz+pmS88uKh73X/W7diWJO0UAGao1cFrWPeuYd2+jnFlwfTnXSe7XToZgWFds2bSfWsm19ClC+B7eMcPAAAAQE8p/AAAAAD0lMIPAAAAQE8p/AAAAAD01Lwe7rz/1KW5/ecv6eS1cebeeW/6YvMaO199RTPfe1b74N4DK7oXH9vTPoR4wU3faeY7XtW9z/G97YMP//j9Vzbz//d//8NO9suv/Ghz7F8+47RmPuSoxZz1N3s62dTq5c2xB05qH2w9fnM327+6XRfc+bL2wcxrbjvQzBeuXdvJVtzfHrv0Ow8387ru5E42+fC25th1X24f7vzQX53fzF/7u93n4Xe+/kPNsbd99MJmftWvfb6TvedLz2+OPeej7WdyYm133ptf0j6Q8uQ/uqeZA8CszfQQ55kc0HuMqFPDDrAespaZHBY8isdjFIchP9F15sqw185MHtdhY0dxjWGOlccJYI4cf/9LDQAAAMC0KPwAAAAA9JTCDwAAAEBPKfwAAAAA9JTCDwAAAEBPzWtXr8UP7s35/+6mTj61c2cn2/wLz2teY8HBdheGS37plma+cMFkJ1v/+u79Jclb1jdaWyX5lc27OtnfbWl3iNq7e1kz3z7Vzf/wD69qjp14YzPOGb/d7nQ2tmt/J/v2v1zSHDt1oN1F4NlvWdfJHvpguy741p///5r5L9/4qmZ+xusXdrJHn7K4OXbJ/Yua+eSd7Q5eLXtevqOZbzj5kWb+rje/opPVZ7Ufp91P39fMP/2b3dfrSf/40ebYn/+Pn2jm//qD/6iTXfiH9zXHTjRTAHgCx3InoRF0sSoL2utrdfAaOnayu2984jsdwWM6gq5Uw9feuM5MH+u57Hg1ig5oM7nGKLqlDb3GMfzzBZzwvOMHAAAAoKcUfgAAAAB6SuEHAAAAoKcUfgAAAAB6alaHO5dS7kqyM8lkkola68ZRTAoAgOHswQCA6RpFV68X1lofns7AfWcvya1vfnInX7j0YCe74Opu968k+fYfntfMv/qfn9HM96/tnrB/1qfaXZ+uvHFFM7/rV5/VyQ6ubHcXe80Lv9DM/8/3/1Qn+6c/86nm2M++unt/yaGdXcuB393dyca+sqY5dvXd7Wvsfmu3q9fkj7THvuEPXt/Ml79oSzOfOntDJ3vswnZHhJU/2u5XtfKfnt7JJu5/oDl27Iurm/nmR1Y185f/ymc72bu/fkVz7KpN7W5pT37jjZ3ss5subo79o7d0u4glSXllN7vlX3XXnSRP/uX2Y133dzu8AdBb096DzcjR6O40TOM+y9jYkKHtvVmr49WwscdrZ6uh65nZRWZ/jVHc50wfj5lc41h6bQPMIx/1AgAAAOip2RZ+apJPllK+Wkq5ehQTAgDgsOzBAIBpme1HvZ5fa32glHJKkk+VUm6ttX7u8QMGm5Grk2Ts5PbHjwAAmJEZ7cGWZNnRmCMAcAyY1Tt+aq0PDP7ckuTDSS5vjLmm1rqx1rpxbOXy2dwdAACZ+R5sYRbP9xQBgGPEEb/jp5SyPMmCWuvOwdc/nOQ3nuh7Fm1Lzvsvrbx7oO/mV1/SvMbCb3cP6EuSFQ+1DwWeXNpd4tTi9rIXTLaPTz75xu6Bb9t+Yk9z7Fcuax84+MobvtTJPvmLP9gce9dPL2zmF/xSM86jHzyjky1a1X6cFky0D//bfUH34OPdF3QP3U6S5d9pz2/sXd0DopOkbrquk51+Tmd/miRZ8WvbmnlWtw9mbtl9VvuAvhX3t8d/+cqzO9lZG9uvkTrWfp1tvrJbQz37ue157Fu/tJmf+391XyNjl1zUHDvpEGeAE9aR7MGGXqtxUPKMDz6eSzO4z9YhzkPHDjsgeshecBRmMr9RaT6Xozg8eURm9Pqbw2vM6DFxEDRwHJrNR702JPlwKeW71/mzWusnRjIrAACGsQcDAKbtiAs/tdY7klw6wrkAAHAY9mAAwExo5w4AAADQUwo/AAAAAD2l8AMAAADQU7M53HnGFp6xP+t/485OfvnquzrZf/rMS9rXeLRdq1q5qd2yadtTz+lkk0vanRzG165t5g82mm+tWXKgOXbvVe1uVR/6VPc+X/p/b2qOLf/y6c18mJ3ndrPFj7THrvvJe5r5PX/bfZxWrn+0OXbX3naHrf1r2s/Nmi+c1sm2Xdx+DlZ/tf0cZO++dt6w7JwdzXzyq6ub+XP/+q5O9o5NZzbHnvKZdkezUz/W/VG65dZ2R4mz/7L9ON3+tis62dTSdueIp7xxeTOf2r27mQPAdA3tPjWKrl5DOiLVqfa1m3MZNo9h8251eBoyj+HdvtqXnsk1hmrNe1h3sRmuvWQGEx8278ZcZtx5a8j8WteZafez+b7GsNeqbl/Ascw7fgAAAAB6SuEHAAAAoKcUfgAAAAB6SuEHAAAAoKcUfgAAAAB6al67ek3enux4RTf/5HnP72RP3ba5eY2T/7TdruqrF5/VzC9af1snu2frBc2xq8rZzfy0C7d2sgfuXNcc+89+62PN/Leue2knu+2nL2yO3XPRoma+spkmV//IJzvZX9z9rObYOza3573yOQ93sse+fVJzbF3W7lpwyXPvaOaPfab7uL7kFV9pjr3qtdc381/99Z/pZKv/S/s1suvhdserMz/0jWb+hb9Y08kuXvdgc+zk1u7jlCQ7P9DthPGUfLM5Nhd2O6glyVP+465OdvDMk5tjF6xc0cx19QKgqZSU8W5nyjI2/X8DrLXdyamUId2PmuPbnaOGdp9qdLEaNueh81vY2OqZuVcAABxsSURBVO7OoIPVE2p1chrWYWsmj9NMO4MN07rOkI5cQ+fXuMZMnq8kKYvaXVFLa+1TM+uONZP+XcNeIwvGZ3CVIdeoBydmMJO2mXYjA/geT9Bw0Tt+AAAAAHpK4QcAAACgpxR+AAAAAHpK4QcAAACgpxR+AAAAAHpqXrt6nfbUHfnVj/5NJ7944b5O9mc7L2pe4733/EAzP+ef3NvMH9vY7Zx1ys3faY6d3LylmU8sv7yTrTup3W3hg//8lGa+5AP7O9k/fF/3sUiSP7nvivb8trQ7df3JO5d0srW3tTsLjC9v1/rWfuahTrbsuWubY1d+vv34HRjSgWLJnls72XW/t7E59tbvXNK+z9J9jYyd3O46tmJdu7NV+djqZv6q0zZ1spv3dJ+vJLl7T/s+f2DNXZ3sS4+c1xz7jeu6XcSS5IdfcGcnW7rg/ubYv/x89zWZJBe8od3pDIATWyml2VmpLFvWHTyks9CCBUP+vXDI//7Xfd3/7R5qYkhHpFZXr8Xt7qfD1IMHu+F4ews8tLPVkPnV/Qe61xg2v2Edr1qdwYaNHdbRbNjj1zI5pGvWkGs3H5Fh9zekY1iWdveqh+bS6A42ZO1DtR6/IYb2zGo9JjN5PNJ+LSSZcZcygCNVJof//vSOHwAAAICeUvgBAAAA6CmFHwAAAICeUvgBAAAA6KlZHe5cSrkyyduTjCX541rrbz3R+AdvXpl/+/S/3/2LC87uRFM3frt5ja1v2dDMl1+8opkvvvfRTlZ37nqCWXaVxrlzO9rn9mb5y9qHT5/141/pZO+/9MXNsWMrFrfn8YXrm/m6hd2Dkpd+u31Q9cM/eEYzn3ioeyjwoxed3xx7cHn3wOwkWfuRm5v5gR94cicb39s+6K584Wvt+f3QszvZgm2PNMeu+rP2/Cb/4pvN/L05vZEOOZww25rp32RlI93aHHv+kLx1ZPbEi7rrTpKLbvhWM2+8VAHooZnuwQ6etzCb33ZmJ3/JWd3/PVk9trd5ja/t6H5/kvzpk/5rM3/Pjvaeo+XLO5/UzB87uLST/cT6LzXH/oMVO5r5Z/Z2/53z2wdObY5dvqDd3GH7ZOMQ7CS/d+MLO9lbLvur5thtk+296guWdfe8f/5YuwnGvzz5q81882T7sOXP7Lmgk31991nNsf983Web+Qd3dJuLPLC/3ajiK5u7e/ok+aNL/qSZf7hx7WEOTrUPEd8+0X1uxlub9yQTtX2NHQe7h08vKI1DwZOctqT9Ovur7zytfZ8H2/fZUuvQ46dPWMN25HBcmOef6X2/2q4jJLN4x08pZSzJ7yd5aZKLk7ymlHLxkV4PAIDDswcDAGZiNh/1ujzJ7bXWO2qtB5K8L8lVo5kWAABD2IMBANM2m8LPGUnufdzt+wbZ9yilXF1K2VRK2XSg7pvF3QEAkCPYg008tmfeJgcAHFtmU/hpfWCt8zHMWus1tdaNtdaNi0r387MAAMzIjPdg46vbZ9QAAP03m8LPfUkefzrcmUkemN10AAA4DHswAGDaSq1HdlZ6KWU8ybeTvDjJ/Um+kuQna63t1k6HvmdrkrsHN9clefiI7vz4YY39YI39YI39YI3HvnNqreuP9iT6zB5sWqyxH6yxH6yxH6zx2Dd0D3bE7dxrrROllJ9L8tc51Er0nU+04Rh8z/+YRCllU6213auyJ6yxH6yxH6yxH6wR7MGmwxr7wRr7wRr7wRqPb0dc+EmSWuvHk3x8RHMBAGAa7MEAgOmazRk/AAAAABzDjmbh55qjeN/zxRr7wRr7wRr7wRph9k6E15g19oM19oM19oM1HseO+HBnAAAAAI5tPuoFAAAA0FMKPwAAAAA9Ne+Fn1LKlaWUb5VSbi+lvGm+73+ulFLeWUrZUkq56XHZSaWUT5VSbhv8ufZoznE2SilnlVI+XUq5pZRycynlFwd5n9a4pJTy5VLK1wdrfMsgf1Ip5brBGt9fSll0tOc6W6WUsVLKDaWUjw1u92qNpZS7Sik3llK+VkrZNMh681pNklLKmlLKB0optw5+Lp/bpzWWUi4aPH/f/W9HKeUNfVpjkpRSfmnw++amUsp7B7+HevXzyLGjj3uwvu+/Enuwvv0+tAc7vl+riT1YH9aYnHh7sHkt/JRSxpL8fpKXJrk4yWtKKRfP5xzm0LuSXPl92ZuSXFtrvTDJtYPbx6uJJG+stT41yRVJfnbw3PVpjfuTvKjWemmSy5JcWUq5IslvJ3nbYI2PJnndUZzjqPxiklsed7uPa3xhrfWyWuvGwe0+vVaT5O1JPlFrfUqSS3Po+ezNGmut3xo8f5cleXaSPUk+nB6tsZRyRpJfSLKx1vq0JGNJXp1+/jxylPV4D/au9Hv/ldiD9e33oT3Y8f1aTezBjvs1noh7sPl+x8/lSW6vtd5Raz2Q5H1JrprnOcyJWuvnkjzyffFVSd49+PrdSV45r5MaoVrrg7XW6wdf78yhX3BnpF9rrLXWXYObCwf/1SQvSvKBQX5crzFJSilnJvmRJH88uF3SszUO0ZvXaillVZIfTPKOJKm1Hqi1bk+P1vh9XpzkO7XWu9O/NY4nWVpKGU+yLMmDOTF+Hpl/vdyD9X3/ldiDpUe/D+3Bkhzna7QH69UaT6g92HwXfs5Icu/jbt83yPpqQ631weTQ/2gnOeUoz2ckSinnJnlmkuvSszUO3n77tSRbknwqyXeSbK+1TgyG9OE1+x+T/KskU4PbJ6d/a6xJPllK+Wop5epB1qfX6nlJtib5z4O3i/9xKWV5+rXGx3t1kvcOvu7NGmut9yf5nST35NBm47EkX03/fh45NpxIe7De/J74fvZgx/1r1h7s+H+t2oPl+F/jibgHm+/CT2lk+skfR0opK5J8MMkbaq07jvZ8Rq3WOjl4W+OZOfSvo09tDZvfWY1OKeXlSbbUWr/6+Lgx9Lhd48Dza63PyqGPNPxsKeUHj/aERmw8ybOS/EGt9ZlJduc4frvtExl8tvoVSf7iaM9l1Aafjb8qyZOSnJ5keQ69Zr/f8f7zyLGhj7/rTyj2YIeGze+sRscerDfswXrgRNyDzXfh574kZz3u9plJHpjnOcynzaWU05Jk8OeWozyfWSmlLMyhDcef1lo/NIh7tcbvGrxl8zM59Fn6NYO3ACbH/2v2+UleUUq5K4fe5v+iHPrXpz6tMbXWBwZ/bsmhzyRfnn69Vu9Lcl+t9brB7Q/k0CakT2v8rpcmub7Wunlwu09r/KEkd9Zat9ZaDyb5UJLnpWc/jxwzTqQ9WJ9+TySxBxv81fH+mrUHSy9eq/Zg6cUaT7g92HwXfr6S5MLBadmLcuitYx+d5znMp48mee3g69cm+chRnMusDD6D/I4kt9Ra3/q4v+rTGteXUtYMvl6aQ78Qbkny6SQ/Phh2XK+x1vqva61n1lrPzaGfv7+ttf6j9GiNpZTlpZSV3/06yQ8nuSk9eq3WWh9Kcm8p5aJB9OIk30yP1vg4r8n/fItx0q813pPkilLKssHv2O8+j735eeSYciLtwfr0e8IerCe/D+3B+vFatQfrzRpPuD1YqXV+371USnlZDlW3x5K8s9b6m/M6gTlSSnlvkhckWZdkc5JfT/KXSf48ydk59OL6iVrr9x9AeFwopfy9JJ9PcmP+5+eSfyWHPmPelzU+I4cO8RrLoaLon9daf6OUcl4O/cvMSUluSPK/1Vr3H72ZjkYp5QVJ/kWt9eV9WuNgLR8e3BxP8me11t8spZycnrxWk6SUclkOHQ65KMkdSf5JBq/b9GeNy3LoTJLzaq2PDbK+PY9vSfIPc6hrzw1JfjqHPk/ei59Hji193IP1ff+V2IOlh78P7cGO39dqYg+W/qzxhNqDzXvhBwAAAID5Md8f9QIAAABgnij8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPSUwg8AAABATyn8AAAAAPTUYQs/pZR3llK2lFJuelz25lLK/aWUrw3+e9ncThMAAACAmSq11iceUMoPJtmV5D211qcNsjcn2VVr/Z2Z3Nmi8WV16aI1nXzfyWPdwWPteY3tKc38otM2N/Nbd6+b/gR3N+aRpEw15rH6YHseS7c387sOrOhkuw4sbo4dW9C4wySTU+063eK793eyifMWNsdOTLSvsWHZzk62ecfq5tgnr3mome+q7fU8tHdVJ6t724/1hrXtx++hnd25lIPt18KiHe3Hb+U5u5v5wztXNvOmIT8uZbIVzuwaCxrXqEOuMdV+erNkc/e1kCR1ojXBo+GJf9/Mm2NkGtBH+7I7B+r+Yb8BAQCYZ+OHG1Br/Vwp5dxR3NnSRWtyxVN+ppPf/pPd/1M/sXqieY2Trm9P+W9/7a3N/HnX/XQnK0O2o3VTu9CxaEc3W/2KB5pjP/O0v2zmr7vn73WyL977pObYlcv2NfPtO5Y18wtef2cne+T3TmmO3ba9W4BKkl+49NOd7G3XXtkc+5dX/W4z/+K+c5r5v7+5e52JG9uP9S+/qv34/fvPvryTLXmg/Vo4+xPdIlaS/P0//kozf+enX9ANS7sysGBYsWl7t6A2uWhm11jcqHlNDfkJ3XNa+9oXvbX7WkiSyUcebV+oZWoOqyK1XZSbb3Uu1zj8Tuf/PmfiMP8IANN1Xb32aE8BAIDHmc0ZPz9XSvnG4KNga4cNKqVcXUrZVErZdGBizyzuDgAAAICZONLCzx8kOT/JZUkeTNJ+C0iSWus1tdaNtdaNi8bb71gBAAAAYPSOqPBTa91ca52stU4l+aMkl492WgAAAADM1hEVfkoppz3u5o8luWnYWAAAAACOjsMe7lxKeW+SFyRZV0q5L8mvJ3lBKeWyHOqNc1eS18/hHAEAAAA4AtPp6vWaRvyOOZgLAAAAACM0m65eAAAAABzDFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAeuqwhZ9SyjtLKVtKKTc9LjuplPKpUsptgz/Xzu00AQAAAJip6bzj511Jrvy+7E1Jrq21Xpjk2sFtAAAAAI4hhy381Fo/l+SR74uvSvLuwdfvTvLKEc8LAAAAgFk60jN+NtRaH0ySwZ+nDBtYSrm6lLKplLLpwMSeI7w7AAAAAGZqzg93rrVeU2vdWGvduGh82VzfHQAAAAADR1r42VxKOS1JBn9uGd2UAAAAABiFIy38fDTJawdfvzbJR0YzHQAAAABGZTrt3N+b5EtJLiql3FdKeV2S30ryklLKbUleMrgNAAAAwDFk/HADaq2vGfJXLx7xXAAAAAAYoTk/3BkAAACAo0PhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAekrhBwAAAKCnFH4AAAAAemp8Nt9cSrkryc4kk0kmaq0bRzEpAAAAAGZvVoWfgRfWWh8ewXUAAAAAGCEf9QIAAADoqdkWfmqST5ZSvlpKubo1oJRydSllUyll04GJPbO8OwAAAACma7Yf9Xp+rfWBUsopST5VSrm11vq5xw+otV6T5JokWb3s9DrL+wMAAABgmmb1jp9a6wODP7ck+XCSy0cxKQAAAABm74gLP6WU5aWUld/9OskPJ7lpVBMDAAAAYHZm81GvDUk+XEr57nX+rNb6iZHMCgAAAIBZO+LCT631jiSXjnAuAAAAAIyQdu4AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTCj8AAAAAPaXwAwAAANBTsyr8lFKuLKV8q5RyeynlTaOaFAAAAACzd8SFn1LKWJLfT/LSJBcneU0p5eJRTQwAAACA2ZnNO34uT3J7rfWOWuuBJO9LctVopgUAAADAbM2m8HNGknsfd/u+QfY9SilXl1I2lVI2HZjYM4u7AwAAAGAmZlP4KY2sdoJar6m1bqy1blw0vmwWdwcAAADATMym8HNfkrMed/vMJA/MbjoAAAAAjEqptfMmnel9YynjSb6d5MVJ7k/ylSQ/WWu9+Qm+Z2uSuwc31yV5+Iju/Phhjf1gjf1gjf1gjce+c2qt64/2JAAAOGT8SL+x1jpRSvm5JH+dZCzJO5+o6DP4nv+xESylbKq1bjzS+z8eWGM/WGM/WGM/WCMAAMzMERd+kqTW+vEkHx/RXAAAAAAYodmc8QMAAADAMexoFn6uOYr3PV+ssR+ssR+ssR+sEQAAZuCID3cGAAAA4Njmo14AAAAAPaXwAwAAANBT8174KaVcWUr5Vinl9lLKm+b7/udKKeWdpZQtpZSbHpedVEr5VCnltsGfa4/mHGejlHJWKeXTpZRbSik3l1J+cZD3aY1LSilfLqV8fbDGtwzyJ5VSrhus8f2llEVHe66zVUoZK6XcUEr52OB2r9ZYSrmrlHJjKeVrpZRNg6w3r9UkKaWsKaV8oJRy6+Dn8rl9WmMp5aLB8/fd/3aUUt7QpzUmSSnllwa/b24qpbx38HuoVz+PAAAcXfNa+CmljCX5/SQvTXJxkteUUi6ezznMoXclufL7sjclubbWemGSawe3j1cTSd5Ya31qkiuS/OzguevTGvcneVGt9dIklyW5spRyRZLfTvK2wRofTfK6ozjHUfnFJLc87nYf1/jCWutltdaNg9t9eq0myduTfKLW+pQkl+bQ89mbNdZavzV4/i5L8uwke5J8OD1aYynljCS/kGRjrfVpScaSvDr9/HkEAOAome93/Fye5PZa6x211gNJ3pfkqnmew5yotX4uySPfF1+V5N2Dr9+d5JXzOqkRqrU+WGu9/v9v735CrKzCOI5/HxyFtEKyP5RTmBDRJtJFRIaIRlCJbYqKAgmiTZsWEdQmCtxFtGujtSrBzMplQQWtJMygyJX90Ulz3Gh/Fv39tXjP4CDWxum9zOn7geG+59x3cR7uOcO9D+c8b7v+ieFH5mr6ijFJfm7Npe0vwGZgb+tf1DECVNU0cC+ws7WLzmL8B93M1aq6FNgI7AJI8luS03QU4zm2AEeSfEd/MU4BF1XVFLAcOMH/Yz1KkiRpJGMnflYDx+a1Z1pfr65KcgKGxAlw5YTHsyCqag2wDjhAZzG2I1CfA7PAB8AR4HSSP9otPczZV4BngL9aexX9xRjg/ao6WFVPtL6e5upa4BTwejuyt7OqVtBXjPM9BOxu193EmOR74CXgKEPC5wxwkP7WoyRJkiZo7MRPnafP58kvIlV1MfA28FSSHyc9noWW5M92tGSaYYfaTee7bdxRLZyq2grMJjk4v/s8ty7aGJsNSdYzHCt9sqo2TnpAC2wKWA+8mmQd8AuL+MjTv2n1bbYBb016LAut1Se6D7geuAZYwTBnz7XY16MkSZImaOzEzwxw7bz2NHB85DGM6WRVXQ3QXmcnPJ4LUlVLGZI+byTZ17q7inFOOzbzMUM9o5XtGAYs/jm7AdhWVd8yHLXczLADqKcYSXK8vc4y1IW5lb7m6gwwk+RAa+9lSAT1FOOcu4HPkpxs7Z5ivBP4JsmpJL8D+4Db6Ww9SpIkabLGTvx8CtzQnliyjGH7/v6RxzCm/cD2dr0deG+CY7kgrQ7MLuBwkpfnvdVTjFdU1cp2fRHDj7LDwEfA/e22RR1jkmeTTCdZw7D+PkzyCB3FWFUrquqSuWvgLuBLOpqrSX4AjlXVja1rC/AVHcU4z8OcPeYFfcV4FLitqpa3/7Fzn2M361GSJEmTV8m4O8ir6h6GHQZLgNeS7Bh1AP+RqtoNbAIuB04CzwPvAnuA6xi+4D+Q5NwC0ItCVd0BfAJ8wdnaMM8x1PnpJcabGQqpLmFIiu5J8mJVrWXYHXMZcAh4NMmvkxvpwqiqTcDTSbb2FGOL5Z3WnALeTLKjqlbRyVwFqKpbGAp0LwO+Bh6jzVv6iXE5Q124tUnOtL7ePscXgAcZnpx4CHicoaZPF+tRkiRJkzd64keSJEmSJEnjGPuolyRJkiRJkkZi4keSJEmSJKlTJn4kSZIkSZI6ZeJHkiRJkiSpUyZ+JEmSJEmSOmXiR5IkSZIkqVMmfiRJkiRJkjr1NwoFByKepzkXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00081401 0.00098463 0.00101628 0.00091138 0.00123188 0.00105776\n",
+      " 0.00105972 0.00095123 0.00092059 0.00108673]\n",
+      "[4.6881025e-05 8.2332670e-05 8.7273082e-05 9.3916795e-05 1.0373414e-04\n",
+      " 7.5807395e-05 7.4994758e-05 6.4378946e-05 6.7834204e-05 9.0254471e-05]\n",
+      "-8.680932998657227\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00081695 0.00128791 0.00119065 0.00112734 0.00087707 0.00075736\n",
+      " 0.00119155 0.00099419 0.00098678 0.00098948]\n",
+      "[5.6517227e-05 8.9282134e-05 9.7465629e-05 9.3258452e-05 6.8757239e-05\n",
+      " 5.7168349e-05 8.6250402e-05 6.7354049e-05 6.6656306e-05 6.7869463e-05]\n",
+      "-6.505446910858154\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00082852 0.00129239 0.00123858 0.00118623 0.00106389 0.00102719\n",
+      " 0.0010266  0.00109097 0.00107985 0.00109118]\n",
+      "[5.54467661e-05 9.63543862e-05 9.41554099e-05 8.67328927e-05\n",
+      " 7.60616313e-05 7.23310732e-05 7.18732190e-05 8.11805512e-05\n",
+      " 9.50062604e-05 1.02789265e-04]\n",
+      "-8.543622970581055\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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nnCrXciYYnVV+bPZfu7w6ZmhZJaHptu9Ux+x5zdOK9cV/fmuxXruWEREHn12+n/sOP6s6pnZvpNvvqY6pGdvzWHlDrR4RvUvLaVutHDt/Tvn4s+ZWxwwcqqfeFfe1dEF1Wxor3+tjc8uJdxER+cqL29pXRMTAtv3Fes+ySkraBfV78/ii8nNt4MXPqI4ZfHhvsT5234PVMcCTSsfWYABA9zpt4yfn/NWIKP8LO+LFnZ0OAAAR1mAAQGe0leoFAAAAwJOHxg8AAABAQ2n8AAAAADSUxg8AAABAQ2n8AAAAADTUmcS5d0zPaI45u0dPqaeReozzrueWo6SPrKiFXEQMX3W0WF/xkXqUc9+R8hyGtpejnMcH65fusaeVj9N3uB4Lvf/y8rahrfXeXC3Mun9vOTL8wCWzq/sa33R/sZ7PX1cdM90Wfa98nvsvK0eJR0TL2PaaZZ97pFg/9U4+IR09Xt1X2nJesd7TIsl8wQPljftvvK4+5i9vq++wNK/15cj6iIixOze2ta+IiDmb9xTredZgdUyeXY4zrz1reh87WN3X3Fwe1fPwzuqY0csuKNa3Pr/+vJmzY1b5+FvLd0fPcP38+w+Wx9Qi2yPEtgMAAKfnHT8AAAAADaXxAwAAANBQGj8AAAAADaXxAwAAANBQGj8AAAAADTW1qV7HRmL2xh2n1PO8egrT/IeHi/Ul776jOmbfzz+rsmW8OqZ3uLxt17Xl1J5a0lJExMErR4r1dKS3OiYWls8zPVo+fivpwOFiff6DQ9UxebScKNT3+fp1nqkWfr98/hERkSppcJUUqIiI0S1b25tA7RgRsfz28n124OL6vVF7Dmx9bj1tqpb4VpM33FPdNvIjzyjW+/++fm8cubycXrbv8nJyV0TEwWuOFetX/s7lxfrhNfWznLWtnPgWC+dVxxxbXk7c6j9UHRL7nla+bw5fWD7PRRvrj/Po7PJ9M+uh+vEBOIda/H3eat0AADONd/wAAAAANJTGDwAAAEBDafwAAAAANJTGDwAAAEBDafwAAAAANJTGDwAAAEBDTWmce4yNRd5/4JTyyMXl6OeIiPGB9ntTC+4vx0JveWE9/npoa/k4h37oeLGexsvRzxERgwvKUdLLP1E//oGLyrHtj68rR8NHRJxfqY/t2Fms73zdxdV9jbzw2cX66t/9dnXM+OEWselT4NBF5es5uK8cTR8RMXteOc577MCp9+UP9K69pFgff2hLsb7t5Sur+xraNVasDxysx8IeWlm+11Z+9Wh1TCfVYtuPvPqZ1TE9I+Xzmfdo+fwjIvqOlM9z7N47i/VZg0+t7qt338FiPR+q37O7n15+Ri2/o/4cPHhJ+XVjyd3l81xw26PVfe177kXFep5bf91I15SvQb7ru9UxAE96UxWzPlWR7a3Op0acfPtq19m1BLqAd/wAAAAANJTGDwAAAEBDafwAAAAANJTGDwAAAEBDnbbxk1J6T0ppV0rpnpNqv5lS2ppS+tbEfz96bqcJANBdrMEAgE44k1Sv90bEH0fE+55Q//2c8zvaOVgeGy+mJw3cv6M6pvfCpeV9tThO/7fvL9YXXPyU6phji8o9sEVfHyjXN5eTwyIi+r48Xqz3HNtfHTN0255i/fBFl1fH1OTRSqpVi9CI0SvLSWR7furp1TGL33NrO9PquIH95eSk/gP1FKZW6V01u5+3vFhfvPmBYv2CT2+r7mvzby0o1i/9rXJ6XETE+D3fazG76TPn47dXt/UuLJ9n9JefTxERQ7t3t3X8g5fNrW6b++H2U60u+s/l+/nYK66rjrnqD8oJegeuLt8z1edmRPQfLr9u7L9yfnXM3EfKyW6TyIcBZqb3RofWYHRYJxOipEpNjdp1nkxKnCQ24EnmtO/4yTl/OSL2TsFcAACYYA0GAHTC2XzGz5tTSt+ZeBvyoo7NCACAVqzBAIAzNtnGzzsj4tKIuDoitkfE79Z+MKV0U0ppQ0ppw0jUf50FAIDTsgYDANoyqcZPznlnznks5zweEe+KiOtb/OzNOef1Oef1/TE42XkCAHQ9azAAoF2TavyklFac9O2rI+Ke2s8CANAZ1mAAQLtOm+qVUvpARLwgIs5LKW2JiN+IiBeklK6OE+FaD0XEG8/kYKmnJ3rmDJ1SH91eT/WKVtsqaslNi+6up2pteUn5V+Tnbi8n7ex8xqzqvoYXlP+v2orb6ok+cyr1+eWAskkZ2lY+l4iInpHyDJZ95qHqmPrZTI2ekfL5bH3BqffYD1z83XJCUqu0r76j7aUwHL6ynEQXEbHkk73F+uZfmF0ds+qz64v1g6v6q2MWPDhcrA98rZx2NX6snlKX+sovE60Sqnb/83KC3pJvt0hVazPVa96Dh6vbJpObceDGZxbri75RTu5qZe595dea1FPvtfeMlmc9+Hg5vS4iYscN5Xt9xddbTA540ujkGqzrzeQUppk8t27Q6lpO5rEBmIFO2/jJOd9YKL/7HMwFAIAJ1mAAQCecTaoXAAAAADOYxg8AAABAQ2n8AAAAADSUxg8AAABAQ2n8AAAAADTUaVO9OimPj8f44XoEczt6L7+0uu3oJYuL9Tnfr8dFX/COjcX6odeWI55X/Vk5FjsiIg+PFOuj111RHXP/jeU5X/qXe6pj6iHPZSNz65GU+9eV479XfrgeAT8lWsRojg6Vo9GPLa/PefgZlxXr/fvqcebzP3BbdVvJ4N9+s7pt9tOvrBzje9UxPeuuKtYHPlO+Z1uZzKPZKra9Zsn7ytdgMvuqGVkwWN1We2GrRdNHRMx/f/lxbvU8O/6K68rHP1Ie1TNUn/PsB/cV68Mr5lfHHF1WPp/x515dHdPz1W9VtwFMi9rf9ZOJLO9kNPpURXlP9/E7qdWcZ2oE/ZPxOjNzPRmfA3QF7/gBAAAAaCiNHwAAAICG0vgBAAAAaCiNHwAAAICG0vgBAAAAaKgpTfWqaZXQlbfvKtbHvn9/dcysR7YW66PH6slNW9/y7GJ99s7yp68//ItPre5rZF55zAVfqScaXfqOe4v1XT/5lOqYJRs3F+u9y5cV66lFPNHlb9hQrHcug2mSWnz6/dwvbSpveO0l1TEDW/YX62ObH6iOOfyacrLb0EduL9bTdT9U3VeunM+jv16+/yIi5myrXIPrnlUdM3CoPGbeX1USyq6vz7l3XzmJr9U1q6V39S5dWh0ztrueulfc15EWd2clUaFVqtjeXyhfz6WfrNxnETH4qUqCW+V6js4bqO6r70A5pa7vcDklMCJibE5/sf7Aa2ZVx1z54AXluW3dVh0DcE51MtVqMvua7lSn6T7+VOlkelsnj8+T05PxfpruOdPVvOMHAAAAoKE0fgAAAAAaSuMHAAAAoKE0fgAAAAAaSuMHAAAAoKE0fgAAAAAaakbEubeKZp+M8Upse372uuqYlW//erG+61+VY7ZXv++h+vEf21us96xYXh2z7afL8fDL7jxUHVMztnNXsd53tB5zvu9Ta4v1836mvK+IiLHHy9HoU+XY+suK9fHj9XjF+95QfgzWvKUeTX5sUbk/OlT5+Z7Nj1T39diPP6VYv/iT+6pjeg4eLdZHH3ioOqZt37i7uimtLMd/9604v+3DjG7fUd/YZsRluvXbbR+/Z1Y95nzxn99aPk6L89z/szcU60PbyxHso3PKke0REWOr55f3deej1THRe3G5fKj+HNj0X8vPgQv/18rqmMG/q8TWA5ypqYrSnqmR3TN1XjNBq2tTi7nu9ljsTt9PT8br3O6cp2pfkzGZ5wC0yTt+AAAAABpK4wcAAACgoTR+AAAAABpK4wcAAACgoU7b+EkpvSeltCuldM9JtcUppc+llDZP/Lno3E4TAKC7WIMBAJ1wJqle742IP46I951Ue0tEfD7n/PaU0lsmvv93p91TSpEGB08p5+PHz2Su/0TveUuq28bXlFOIji059dg/8Mj/uL5YX/u/DhfrW16zurqvvmPlpJ3ROfVPbF/xlQPF+rGls6tjamfTe1U5oWtX+RQjImLp+8vXc+zxzfVB0+zxywbKG4bHqmPmVcK7jvzzZ1bHHF/Y3if6jx8tp8pFRJz3xXJC0+ijW+r7a+vok3PodeV0qoiIhbdvLdbzkXJyVUTElv+tfA8uvO+i6pjZn/hGdVtJ36oLq9uOrS0nVw3sKj+fIyLi3vK93iqJbPHflBPXxi8tz23/mnJyV0TErH3lR3pkdT0NMPeUkx5yPTwsVn6gv1gfHarf5/VXTuAce290ag1Gs8zktKVOkobWvskkVM3U6zwTUrWmW7c81znnTvuOn5zzlyPiifnkr4yIWya+viUiXtXheQEAdDVrMACgEyb7GT/Lc87bIyIm/lzWuSkBAFBhDQYAtOVMftXrrKSUboqImyIiZsWcc304AADCGgwAOGGy7/jZmVJaEREx8eeu2g/mnG/OOa/POa/vT7MmeTgAAGKyazCf1gUAXWuyjZ9PRsTrJ75+fUR8ojPTAQCgBWswAKAtZxLn/oGIuDUirkgpbUkpvSEi3h4RL0kpbY6Il0x8DwBAh1iDAQCdcNrP+Mk531jZ9OK2j5bzpKLbS8b2PFbf+NgTAzBOGHldPbL78n91W7H+4G8/q1hfelc9ZHvPunLs3kWfLkc/R0Rs/pVyxPLST9dzmWtv2h7bWI6l7jl+XnVfR5eW57zzPeurYy7/lxuq26bCvmtGi/VFKw5Ux8z/63nFeu6txzgO7G/vjXH7f/Ka6rYdzyvfNwOPraqOWfOJQ8X62Jz607d/z5FiPT+yrVif+6Hy/R8RkRcuKNbT4kXVMSt+9+vFeu/ll1bHjFW3lI0+uqW6bWDvvmI9DQxUxxx6zXXF+s7r63O44h0Plsc8q3zN+o7Uozd7h8vb7vvF+mtA37bytt7j9fv5kVeV78G5m+rXZqi6BTiXOroG66SZHH38ZJQq64xcX2tOu9o9UDuXiJl9Ppx7rV43JhNNPt3Pm1b3eo3nANNosr/qBQAAAMAMp/EDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANddpUr6nQM6+ctBQRkc5fWt6wu57qtfN1TynWZz9W/yT1nqvLY/oOlT+BfuHXHq7ua8E9c4v1fesWV8dc9rPlVKWH/+Oz68ep1I++shxDNLSt/mn6f/DLf1qs/9efeG11zPY3l+e27I/LiU6dNrCwnBB35ZJd1THffGH5MTj/9vq9sfSb5ZSw7/9xOSVuzcdGqvt6ym/tKNZHLqonro0PlJOb+jZ8vzpm7PDh6rZ25YtWlOsPbq2OOfTa8rWpJVdFRGz51+VrsPZf316s912yurqv0QceKtbHrr+iOmbB5zYV6wu/UsvPi+j/YLm+6D+X781HX1JPznrsmvLzc/GX6sdf93/cXawv7C+nukVE3P7b5fSy3uPllDyAGafTyVHTnQ7UyeN0Ojmp3X1Nd+CbxLnpN5nHYDJjas+b6X4+wwzlHT8AAAAADaXxAwAAANBQGj8AAAAADaXxAwAAANBQGj8AAAAADaXxAwAAANBQMyLOffzgwfrGVtsqlt51qFg/snJ2dcyOZy8s1i/69P5i/cAzV1X3Nfe+8phF39lXHfPgh3+oWF/8kfajB2d/4hvF+k/fu7s65t/+pzcW64vuLcfMR0T0r7+hvYl12NzPDxXrl/yrPdUx9+wux0XuX1OOTI+I2PLPyse5/E3lmPHep9Yjw4+vXV6s9x0cro45tqx838656ILqmNi4uTy3hQvKPz9YjwxPB48W6+Pj9Xtz7ofL1+b4K8pR4hERa/91+b6NnvJjU4tsj4jIz15XrB+4qH6es+euLdbnfHFjdcyxX7moWF/8hw8X61u+Vj5GRMTQw+XzHC6/NEVExLd2l++Bw3ctqY5ZtaP8eD5+ef31cVZ9CsCT3UyOv24V297umE7GvLfa36SirLskfnq6z3My91NHVc4z5/qQmfz87KTJPDa1+6bdmPepNN3PAbraDHgGAAAAAHAuaPwAAAAANJTGDwAAAEBDafwAAAAANJTGDwAAAEBDzYhUry1vfXZ12/DC8ifdr715R3XMIy+aV6wv/+bx6pjlf/ftYv3yr5U/Zf2Z8x6o7uvawUeL9V942/9VHTP0mfKn9r/krV+ujrntg/3F+qP/d1SOT04AACAASURBVPl6vud36qkBi79zoFg/9rL11TGLbrm1WB9/3jXVMT1fuau6rST1D1S3zdsyWqzfs7+ednVsafkarP7EkeqYhffXk6BK7vvZxdVtQ08pJ7v1941Vxzx+d/l+Hn3d/PokDj2zWL70w+X0sL5vbqruavzCpcV62lVPT+u9qpxeNXRP/XlbfjQjYrxyba4vJ+FFRNz/mnJC1fwH6ukYL/3V8v38/q/VX5+ueFc5dXDzRy8v1vueW36eRUQ89XnbivU7vlpPibtuaXnM8173xeqYt1/5z4r1VX9yrDoG4KxNd6LOVB2/ltDUybSviOlPAZqqtKl2H7fpvi6TmUOnr2Vtf51Mzuq0ySRxTcVzutX5d/J6zuTnOjNX6bneIiTQO34AAAAAGkrjBwAAAKChNH4AAAAAGkrjBwAAAKChzurDnVNKD0XEwYgYi4jRnHP9k4ABAOgIazAA4Ex1ItXrhTnnerzPScYWD8WBl99wSv3C//L1tg9az0CKWPmlcgrS3quGqmOG/89LivVVY48U63/xsz9a3dd/fPXcYn3kufVZp1nlbZ9+xw9XxyyMcgrRmnffX6w/8MZLq/t629s+Xqy/9e5XV8fMuvBZxfqSd5XnNRl5pJxCFRGx65pyqtnzF26pjtn+3fLj3Hu4nvj26E+UjxOvuK5YvvgT1XyqSOuPFutb7qwnkV3y14eL9T1Pr9/P591cfgxGfuQZxXrf2our+xpZOKtYH1x5fnXM936xnGyW++sfNX/jDQ8X63f+7+X0rtGhyuMSEVf8j53F+uErywllERF3vbp8b/TdVH9j5G9//M+L9Z+5+VeL9UvOe6y6rw1fL6d3rf3A/uqYr1xcnvMdX396dcylH32oWN/85vo9sOZL1U3A9DrjNRh1qaf9VKVcW9JNJlFpMkk/k1GbWy2JrMNq1zmP+wWEqto90Omkp2lPIuvceabe3squJnGfd/w5OEWP55PNVKUEdjmvtAAAAAANdbaNnxwRn00p3ZFSuqkTEwIA4LSswQCAM3K2v+r1nJzztpTSsoj4XErpeznnL5/8AxOLkZsiIgbmLDrLwwEAEG2uwWbFnOmYIwAwA5zVO35yztsm/twVER+PiOsLP3Nzznl9znl936z6Z5IAAHBm2l2D9cfgVE8RAJghJt34SSkNpZTm/eDriHhpRNzTqYkBAHAqazAAoB1n86teyyPi4+nEp3D3RcT7c86f7sisAACosQYDAM5YylMU4RgRMb9nSb5h8OWn1Le9uRwxHRFx6JJyXuZFf1OPvXv4n5frl6wpRzxHROz+uwuL9d5j5Z8//yt7q/s6ePmCYn3n9fU3WP3mKz9UrL/r4edVxwy9sfzYHXtXuf4bl3yyuq+3v+K1xXoeqPcGD6+eV91W03e0/Lj1f3ZDsd573pLqvjb/28uL9bHz69HsQ/PKD+jx75Ufs4iIt776o8X6h175w8V6GqnHuW9/6YpiffGm+pyPLyrHlvcerz8HBj/1zfKYp5Sv2dGL6ue/9efLc7vsbQerYw6sW1asb3tRdUis/aXbi/Xey9aU9/Xy8rWMiBg4UH4O9A7XX+92Pqe87co/qkewx/ZdxXLf/1v+LI3vf6l8LhERo7PL9cvXl2PuIyI2PlS+BvMXHamOWbtkd7F++Efr9+D4wfpjDU90e/58HMh7ZbPOMPPT4vzMnh/pzM46HXFcPcwkYtYrkc2t9tXJMVMWzV4zzbHQtSjtyZpUBHebJnOftdL2vdHKVN03NR2ccyefz6cZVD5+i3tzMsepnU/H79l2H4Op+re9CPYZ6fbxv6+uwcS5AwAAADSUxg8AAABAQ2n8AAAAADSUxg8AAABAQ2n8AAAAADTU2cS5t23s0oHY+4cXnVJf9aZHqmMOP62cWnN8Yf2T2Rct31es9//7enLRBVFOrbnvdXMrx19c3dfxJeVPX5+9+kB1zPufv75Y3/7menLRvEpC0pEvlT9l/e2/WIk7i4ixzZuL9Z455XSiiIjZg5eWN3zj7uqYtrX4ZPrVnyondL3wj75eHfNndz2nvGFe/RPz3/OWVxXrcx9/qFjf+9562ln6eLl+/+vqT8XZSw8X6723z6+OufAL5cct95efN3uvKCeHRUQs/Lvy3EbOr/eNt76k/Lhd8T/L5xIRkW94erE+dtt3yj/fW39u7H7BcLF++UX1ZL/DB8uP29im+6pj8rPXlcc8/9vF+lO+XE4pjIi4+9bLivXh/3R+dUz/TeUEueG7FlXHHPpU+R7Y9N/qz/XL3/SN6jbgyW2qkm46mk7UIuWmej4tEoVSaj+FqZ7oM4lEo8lcm8rcasdvNWbKTOI8U+32rF3Psfrfs1G7N1rd55NIokpRnsOk7o2KVs/N6v5aJa5Vk8jaT66a1GvAJJK46spjcot7o5PJbpNKA2y9w9rOKj8vbYsy7/gBAAAAaCiNHwAAAICG0vgBAAAAaCiNHwAAAICG0vgBAAAAaCiNHwAAAICGmtI493ygL4Y/s/SU+uGnjVTHDC8oR/LNfqwcYxwRsWNPOZZ5/0/XT/dHnlOOX14/cKhY/+q/v6G6r/Pe+mCx/upld1XH/Ie3vLZYX3Z7PfZv4d2PF+tp+65i/YF3rqzu6++e+dVi/b/veUF1zHn93yzWP/sffrg6Zt6GrcX6+P5y1P3olRdV93X/6waK9T3/88XVMfNrKa8j9et83a/fXqz/swXl2Prf2PwT1X1d/S/LY1bM2l8d84Uda8tjfmJHdczm0auL9eFnHyzWf/vq91X39av/cGOxvugvyucSEXHL++4o1m99TvlcIiL+3ZLNxfqlH3xTsb5gU/0x659df02pOXbvwmL9gtvKrycREX9+0S3F+ovvLd8DR35tQXVfqxaUI+gfv2SwOmbhp8v1xX9Vvv4REePPuLJYv/wXy89noCFSitTXf2q5t1XEcvl1Nk0mLrhVbPxofU1X2Vl9UyUuuWUsdG3TeIv4857KdRtp91xaaBUxXYsG728xphJnPZmI6VpkdRqs/501GW3fG5OJ/25xnWv3eq48N1rNoRbz3irmPPWX/+2SWtybeaz9aPRcm1vlyZFSi/OfVDR6ZW6TuTcrr2lp4NTXv/9f5fFsGQE/mdfBVvdNTfV56/0btMcdAwAAANBQGj8AAAAADaXxAwAAANBQGj8AAAAADaXxAwAAANBQqeWn0nfYM9YN5ts+feEp9Vds+vHqmPu2LyvW33H9h6tj5vUcLdbfteP51TEHakFMy5YUy+nA4eq+9rz44mJ971OrQ2L13x4r1h9+U/1T+992zaeK9a/sv6JYv/1D66r7WvG7Xy/WW6UzDD//h4r13VeX07YiIvoPlu+38z9yX/n4LT6B/95fP/Veioj4Xy/90+qYR0fKj+e6wXLaWETET/3hvynWV3y1nJC14zn1FKiV7/tesX583ZrqmOEF5USHob+pp8TlkXJCVO9Ty/fGAz+1uLqvwaeX0+Pm/cX86piDq8rpDOMtcgRXvXdTsT66tvw4P/xjc6r7uvhTR4r1A2tmV8fMf6j8HExf+1Z1TO35sesXri3W/8Ov/UV1X/+w/6pi/evvXF8dc8Ob7izWv7P3guqYLfeVX1N/7rnlZL+IiNvWtUjCgCe4PX8+DuS9k4l14Rxa0HtevmHOj51ST3Pqr6UxXk+0qaok3aRZs+pjKslN7ad9RUQl0SharCeqyU0jLRIieyopRLU5t0pPq6mdS0REbrGtNqS2v1bpZTWVVLM0p/73bOqvPAYtkpNybW61tKdW16WWatUquamvvHCpXsuI+vOmlvjWKgWrv7ymbpXGV71va/dsRP261f6d2Cptq/Zca3Gdq49Nq+O0+/rQ6jpXHueWx689zq2uc21Mq+NU5p2PltetT0qTSUij6LYjfxP7x/YUL6h3/AAAAAA0lMYPAAAAQENp/AAAAAA0lMYPAAAAQEOdVeMnpfSylNKmlNJ9KaW3dGpSAADUWYMBAGdq0o2flFJvRPxJRLw8Ip4SETemlJ7SqYkBAHAqazAAoB0twpVP6/qIuC/n/EBERErpryLilRFxb23Afd+dFz9+1QtO3XB8T/Uglx4vx2z/6WA9Gz0fP16sD7/s0uqYgce+Wayni1aUj7Fxc3VfSz5TjhfsGbmkOqbnS+Vo7jVfqg6Jv4xyzHXfygXF+oqt5cj2VmrXMiKi/7MbivULPtv2YaIW8JiuqT/O875fvn3f9tdvrI7pGS7HVQ5uqD+eFxz5RrGery3Hb0eLRMZ8rHw9U4sYxyPLyv3ZOZXI9laGlw4V65d8cG91zN7vLyrW52+sj5m9q3ycvjvKke0REWNHyhHs/ZUI4OFFK6v76t+xvzyvefU438MXlKPZFyypR92PPVa+Bkv/9NZi/Z1/ell1XxHl+NUlUd5XRMTmd5frs+PB6pi1lW23hch2eBJpew02vGYgtv7eRafUX7Kq/rr84OElxfqs3nqM8vB4+e+sZy76XnXM2sGdxfqth8qvmU+dvaW6rw2H1hTrV8zZUR3z43M3FusfO/i06pjj4+XXzI8/uq5Yv2H5Q9V9Leor//13YLT8919ExOMjc4r17UfnV8c8/Fj577Ph4+VzGTtWj6XunV2+B97/rHdVx4zk8v4Oj5f//o2I+MaR8tp9sKf8d+b3D59f3dc7Vv59sf7Bg/V/Hzx1sHyvrRuor8H+fP8VxfqHt1xbrK+e/1h1X1cOPVKsv3p++d8NERF/sOvFxfovnPfV6pjvHi+vqbYMl++Zv9ta7zPfdvVHivWb919QHbO6f3exvrLvQHXMJw5cXd1W0psqkfUR0VPZdmis/hzcPzq7WJ/bW/+3U+15u2e4vG6OiDgyOlCs3725/u/Kqlpq+mTS1FOLf/DUhkzRcSZ1PrVdTeb41Z11blc1x95Wfz09m1/1WhkRj570/ZaJGgAA5441GABwxs6m8VPqWZ3SEksp3ZRS2pBS2jA8fuwsDgcAQExiDTZ2oPyuEgCg+c6m8bMlIlad9P2FEbHtiT+Uc74557w+57x+oKf+djkAAM5I22uw3vnlXzEAAJrvbBo/34yItSmlNSmlgYj4FxHxyc5MCwCACmswAOCMTfrDnXPOoymlN0fEZyKiNyLek3P+bsdmBgDAKazBAIB2pJw7+EnVpztYSrsj4uGJb8+LiHqcV/N1+/lHuAbO3/k7/+7W1Gtwcc556XRPgn/KGuyfcP7dff4RroHzd/7Ov5mqa7Apbfz8kwOntCHnvH5aDj4DdPv5R7gGzt/5O//uPf8I14Dp0+33nvPv7vOPcA2cv/N3/t13/mfzGT8AAAAAzGAaPwAAAAANNZ2Nn5un8dgzQbeff4Rr4Py7m/PHNWC6dPu95/zp9mvg/Lub8+9C0/YZPwAAAACcW37VCwAAAKChpqXxk1J6WUppU0rpvpTSW6ZjDlMppfSelNKulNI9J9UWp5Q+l1LaPPHnoumc47mUUlqVUvpCSmljSum7KaVfnqh3xTVIKc1KKX0jpfTtifP/jxP1NSml2yfO/4MppYHpnuu5lFLqTSndlVL6m4nvu+38H0op3Z1S+lZKacNErSueAxERKaWFKaWPpJS+N/Fa8KxuOf+U0hUTj/sP/juQUvqVbjl/Zo5uW39FWINZg1mDRXT3Gqzb118R1mDWYCdMeeMnpdQbEX8SES+PiKdExI0ppadM9Tym2Hsj4mVPqL0lIj6fc14bEZ+f+L6pRiPi13LOV0XEDRHxSxOPebdcg+MR8aKc87qIuDoiXpZSuiEificifn/i/PdFxBumcY5T4ZcjYuNJ33fb+UdEvDDnfPVJEZLd8hyIiPjvEfHpnPOVEbEuTtwLXXH+OedNE4/71RHxjIg4EhEfjy45f2aGLl1/RViDWYNZg0VYg3Xz+ivCGswaLKbnHT/XR8R9OecHcs7DEfFXEfHKaZjHlMk5fzki9j6h/MqIuGXi61si4lVTOqkplHPennO+c+Lrg3HixWZldMk1yCccmvi2f+K/HBEvioiPTNQbe/4RESmlCyPiFRHxZxPfp+ii82+hK54DKaX5EfHDEfHuiIic83DO+fHokvN/ghdHxP0554ejO8+f6dN1668IazBrMGswa7Cirrj/I6zBnqCr12DT0fhZGRGPnvT9lolat1mec94eceIv5YhYNs3zmRIppdURcU1E3B5ddA0m3mL7rYjYFRGfi4j7I+LxnPPoxI80/XnwBxHxbyNifOL7JdFd5x9xYqH52ZTSHSmlmyZq3fIcuCQidkfEn0+81fzPUkpD0T3nf7J/EREfmPi6G8+f6WP99Y+68rlnDWYNNvF9t63Bunn9FWENdrKuXoNNR+MnFWqixbpASmluRHw0In4l53xguuczlXLOYxNvMbwwTvxf16tKPza1s5oaKaUfi4hdOec7Ti4XfrSR53+S5+Scr40Tv2bxSymlH57uCU2hvoi4NiLemXO+JiIORxe8pfaJJj5D4Sci4sPTPRe6Uje+7jLBGswa7ORy4Ucbef4Tunn9FWENFhHWYBHT0/jZEhGrTvr+wojYNg3zmG47U0orIiIm/tw1zfM5p1JK/XFiwfGXOeePTZS76hpEREy8tfKLceL37BemlPomNjX5efCciPiJlNJDceJXC14UJ/7vU7ecf0RE5Jy3Tfy5K078bvH10T3PgS0RsSXnfPvE9x+JE4uQbjn/H3h5RNyZc9458X23nT/Ty/rrH3XVc88a7ARrsO5cg3X5+ivCGuwHun4NNh2Nn29GxNqJT5MfiBNvufrkNMxjun0yIl4/8fXrI+IT0ziXc2rid4nfHREbc86/d9KmrrgGKaWlKaWFE1/PjogfiRO/Y/+FiHjNxI819vxzzv8+53xhznl1nHi+/0PO+WeiS84/IiKlNJRSmveDryPipRFxT3TJcyDnvCMiHk0pXTFRenFE3Btdcv4nuTH+8S3GEd13/kwv669/1DXPPWswa7BuXoN1+/orwhrsJF2/Bks5T/07+1JKPxonus29EfGenPNvTfkkplBK6QMR8YKIOC8idkbEb0TEX0fEhyLiooh4JCJem3N+4ocPNkJK6bkR8ZWIuDv+8feL3xonfse88dcgpfT0OPGhYb1xotn6oZzzf0opXRIn/u/L4oi4KyJ+Nud8fPpmeu6llF4QEf8m5/xj3XT+E+f68Ylv+yLi/Tnn30opLYkueA5ERKSUro4THyw5EBEPRMQvxMTzIbrj/OfEic9XuSTnvH+i1jWPPzNDt62/IqzBrMGswX6gG9dg1l8nWINZg0VMU+MHAAAAgHNvOn7VCwAAAIApoPEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANpfEDAAAA0FAaPwAAAAANddrGT0rpPSmlXSmle06q/WZKaWtK6VsT//3ouZ0mAAAAAO1KOefWP5DSD0fEoYh4X875aRO134yIQznnd7RzsIH+oTxr1sJT6scX1ftPPcO1ibU4UOWUxueMV4fMHhgp1o8eHSjW+wdHq/saOdpfrPcMjlXHXDh7X7H+6JHF1TG1h65/X/nijMxrcdF6ahetPiZVTqenfCkjIqLvcHlQGinX82h9Z6m/fJ3nXna8Omas0uscHa/fgwePzSofv3bNjtf3tXbxjmL9/qPnVcfMHyifz9Leg9Uxm48sLdZ79/UW62OD1V1FnlV+3qyYs786Zsfji4r1RQsOVcccGC5f59GR8pz764ePKy/aU6zfe6Q8r4iIOX3le22ot34/7Tgyvz6JTmnxHKxuq92bERFj5TE99Ze0SJWXzr5dh+uD2pVavahXhnRyf6131t6+JjlkinZ2zg9zdPRgDI8fnaJJAwBwOn2n+4Gc85dTSqs7cbBZsxbGddf+0in1+19T/1fn3IfL/4geK/8bMSLqzaKj1x6pjnn6hVuL9bvuWVOsr7p0d3Vf2+5ZXqzPWlP/h/o7nv6RYv1X73pddczw8fLDt/Ij5YbI1ufXGxLj88v/6kuH6rfIwP7yun7O9uqQWHbbgWK9d/fjxfrYjp3VffVesKJYf9YHN1fHHKzcOHuOz62O+cKmy4v1voFKE+uBOdV9fexn/1ux/tpvv6E65iWrNhXrb1r81eqYV3zzjcX6go+Wz/PxtfV7Y3jt0WL916/7VHXMf/n4Txbrr3t5fc6f3Xplsb57x4JifdUn63P+8jtvLtav3fBT1THPWL6lXJ/3UHXMf7vjpdVtJam33pCp/St59Ej9OdhTeX6Oz643mfv2l8cMVBrGERH9lX7d8j/6enVMVU+5kZd6y/UTY8pzS62aOz2V+6MyZjLHj9TiTbO1Ma3UzqfVcTp4/JbXsz7olNLXd3+w/f0AAHDOnM1n/Lw5pfSdiV8Fq/9vdAAAAACmxWQbP++MiEsj4uqI2B4Rv1v7wZTSTSmlDSmlDcMjHfy1AAAAAABamlTjJ+e8M+c8lnMej4h3RcT1LX725pzz+pzz+oH+ocnOEwAAAIA2Tarxk1I6+cNVXh0R99R+FgAAAIDpcdoPd04pfSAiXhAR56WUtkTEb0TEC1JKV8eJ/KyHIqL8SbIAAAAATJszSfW6sVB+9zmYCwAAAAAddDapXgAAAADMYBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUKdt/KSU3pNS2pVSuuek2uKU0udSSpsn/lx0bqcJAAAAQLvO5B0/742Ilz2h9paI+HzOeW1EfH7iewAAAABmkNM2fnLOX46IvU8ovzIibpn4+paIeFWH5wUAAADAWZrsZ/wszzlvj4iY+HNZ56YEAAAAQCf0nesDpJRuioibIiIGBxec68MBAAAAMGGy7/jZmVJaEREx8eeu2g/mnG/OOa/POa8f6B+a5OEAAAAAaNdkGz+fjIjXT3z9+oj4RGemAwAAAECnnEmc+wci4taIuCKltCWl9IaIeHtEvCSltDkiXjLxPQAAAAAzyGk/4yfnfGNl04s7PBcAAAAAOmiyv+oFAAAAwAyn8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo/AAAAAA2l8QMAAADQUBo//1979xaqWVnGAfz/MONYTZYdTGLGDoJUc5GTDWIY4iFETbSLAqVAIpibLgqKsG6iwItuOlxIMKjlRWpmWdKFJGbUlTkeIkulSSyHMafoZAZN1tPFtwY341A6355v2Vq/H2y+9b577b2eZ32zBuY/610fAAAAwEQJfgAAAAAmSvADAAAAMFGCHwAAAICJEvwAAAAATJTgBwAAAGCiBD8AAAAAEyX4AQAAAJgowQ8AAADARAl+AAAAACZq4zI/XFWPJXkqyb+SPNPdO9ajKAAAAACWt1TwMzinu/+wDr8HAAAAgHVkqRcAAADARC0b/HSSH1TVvVW183A7VNXOqtpdVbsP/PPpJQ8HAAAAwPO17FKvM7t7X1W9LskdVfVwd/947Q7dvSvJriR5xXFbesnjAQAAAPA8LXXHT3fvG173J7k1yenrURQAAAAAyzvi4KeqNlfVcQe3k5yf5MH1KgwAAACA5Syz1OvEJLdW1cHfc0N3J7q+PgAABpFJREFU374uVQEAAACwtCMOfrr70SSnrmMtAAAAAKwjH+cOAAAAMFGCHwAAAICJEvwAAAAATJTgBwAAAGCiBD8AAAAAEyX4AQAAAJgowQ8AAADARAl+AAAAACZK8AMAAAAwUYIfAAAAgIkS/AAAAABMlOAHAAAAYKIEPwAAAAATJfgBAAAAmCjBDwAAAMBECX4AAAAAJkrwAwAAADBRgh8AAACAiRL8AAAAAEyU4AcAAABgogQ/AAAAABMl+AEAAACYKMEPAAAAwEQtFfxU1QVV9UhV7amqK9erKAAAAACWd8TBT1VtSHJ1kguTbEtyeVVtW6/CAAAAAFjOMnf8nJ5kT3c/2t0HktyU5NL1KQsAAACAZS0T/GxJ8via8d5hDgAAAIAXgY1L/GwdZq6fs1PVziQ7k+TYY1+5xOEAAAAAeCGWueNnb5KT1oy3Jtl36E7dvau7d3T3jk3HbF7icAAAAAC8EMsEP/ckOaWq3lxVm5JcluS29SkLAAAAgGVV93NWZz3/H666KMmXk2xIcl13X/U/9v99kt8Mw9cm+cMRH/z/39z7T5wD/etf//M21XPwxu4+YewiAABYWCr4WerAVbu7e8coB38RmHv/iXOgf/3rf779J84BAACrscxSLwAAAABexAQ/AAAAABM1ZvCza8RjvxjMvf/EOdD/vOkf5wAAgKNutGf8AAAAAHB0WeoFAAAAMFGjBD9VdUFVPVJVe6rqyjFqWKWquq6q9lfVg2vmXl1Vd1TVr4bXV41Z49FUVSdV1V1V9VBV/aKqPjbMz+IcVNVLquqnVfWzof/PDfNvrqq7h/6/WVWbxq71aKqqDVV1f1V9fxjPrf/HqurnVfVAVe0e5mZxDSRJVR1fVbdU1cPD3wXvmkv/VfWW4X0/+PXXqvr4XPoHAGBcKw9+qmpDkquTXJhkW5LLq2rbqutYsa8nueCQuSuT3NndpyS5cxhP1TNJPtHdb0tyRpKPDu/5XM7BP5Kc292nJtme5IKqOiPJF5J8aej/T0k+MmKNq/CxJA+tGc+t/yQ5p7u3r/kI77lcA0nylSS3d/dbk5yaxZ+FWfTf3Y8M7/v2JO9M8vckt2Ym/QMAMK4x7vg5Pcme7n60uw8kuSnJpSPUsTLd/eMkfzxk+tIk1w/b1yd530qLWqHufqK77xu2n8riH3xbMpNz0At/G4bHDF+d5Nwktwzzk+0/Sapqa5L3JrlmGFdm1P9/MYtroKpekeSsJNcmSXcf6O4/Zyb9H+K8JL/u7t9knv0DALBiYwQ/W5I8vma8d5ibmxO7+4lkEYwked3I9axEVb0pyTuS3J0ZnYNhmdMDSfYnuSPJr5P8ubufGXaZ+nXw5SSfSvLvYfyazKv/ZBH2/aCq7q2qncPcXK6Bk5P8PsnXhuV+11TV5syn/7UuS3LjsD3H/gEAWLExgp86zJyPFpuBqnp5km8n+Xh3/3Xselapu/81LPPYmsVdb2873G6rrWo1quriJPu7+96104fZdZL9r3Fmd5+WxTLXj1bVWWMXtEIbk5yW5Kvd/Y4kT2eGy5qG51hdkuRbY9cCAMB8jBH87E1y0prx1iT7RqhjbE9W1euTZHjdP3I9R1VVHZNF6PON7v7OMD2rc5Akw/KWH2XxrKPjq2rj8K0pXwdnJrmkqh7LYmnnuVncATSX/pMk3b1veN2fxfNdTs98roG9SfZ2993D+JYsgqC59H/QhUnu6+4nh/Hc+gcAYARjBD/3JDll+ESfTVnc9n7bCHWM7bYkVwzbVyT53oi1HFXD81yuTfJQd39xzbdmcQ6q6oSqOn7YfmmS92TxnKO7krx/2G2y/Xf3p7t7a3e/KYvr/Yfd/cHMpP8kqarNVXXcwe0k5yd5MDO5Brr7d0ker6q3DFPnJfllZtL/Gpfn2WVeyfz6BwBgBNW9+tUVVXVRFv/jvyHJdd191cqLWKGqujHJ2Ulem+TJJJ9N8t0kNyd5Q5LfJvlAdx/6AOhJqKp3J/lJkp/n2We8fCaL5/xM/hxU1duzeHDrhizC1pu7+/NVdXIWd8C8Osn9ST7U3f8Yr9Kjr6rOTvLJ7r54Tv0Pvd46DDcmuaG7r6qq12QG10CSVNX2LB7uvSnJo0k+nOF6yDz6f1kWz7c7ubv/MszN5v0HAGA8owQ/AAAAABx9Yyz1AgAAAGAFBD8AAAAAEyX4AQAAAJgowQ8AAADARAl+AAAAACZK8AMAAAAwUYIfAAAAgIkS/AAAAABM1H8AdcCv2DAOqA8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00085159 0.00104343 0.00096727 0.00098759 0.00097229 0.00109307\n",
+      " 0.00116814 0.00105537 0.00114332 0.00103451]\n",
+      "[4.89696868e-05 8.78859137e-05 7.68891259e-05 7.81037306e-05\n",
+      " 8.39475979e-05 1.10460336e-04 9.84411599e-05 8.00350172e-05\n",
+      " 8.14577725e-05 7.35723224e-05]\n",
+      "-7.273463726043701\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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aDAA4fXUs/OScvxjlssxLp3c4AABEWIMBANPjuLp6AQAAAHDqUPgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGmkw79+nb2aGRGNy4Y8LYoYuXV3P3ra+0VZ9T7wW/4KFyLN321WruWYPPK8Z2XFVvRT20rNxm/sIb91Zzc6vcpnxk6UA1t/9gvd101WilLXel1XtERHv//intMu8/UI0fXXRmMbZoU3m8nYw+sb0aT6vL5+TjL6pfOus+UY71DNSPX3poWzHW3lluMR8RMfqSq4qxedvr10mMledydEH5XI6I2H1J+XxtHV1YjPXvHalud3ThnGKs78K11dy44+5iKPfVj9++8+cWY8u/truae/CCxeX9turHQMt2AOCESB3Whbm+9gNOLV7xAwAAANBQCj8AAAAADaXwAwAAANBQCj8AAAAADaXwAwAAANBQCj8AAAAADaXwAwAAANBQvSd0bylF9Exca5q3+WA1dXTuomJswYdvq+a2Lr2wGGtXMyP69o8UYyOL6tPXnjdWjOX+eu6RM+YVYwfXtKq5qz5yX3lM1cyIPHdOOXiwfoymavSis6vx9kA51hrK1dzWovJ5k5YNVnNjz4Fi6Pw/eaKaWpvnNL98bCMiDl+9thib84knq7lztuwrxlbe8mA1N/X1F2OrvzRazY3KYZj7r98pxva/6tnVzfZUJnLOQzuqueWrL2L47CXV3KV3lY/90JqF1dx9a8vX9poHyscnovP1CcAkpTT13FxfW0AjOM/htOIVPwAAAAANpfADAAAA0FAKPwAAAAANpfADAAAA0FAKPwAAAAANpfADAAAA0FAntJ17e6AVBy9ZOmFsaEm9Rfm8J8vtpHvXnVvNHb37/mKstWplNXf3hXOLsfYZw9XcOXPLreAf+OkF1dzBu8ptSPdcWm+/OHj52mKs9fnd1dx0eKgYG7mkvN2IiHTbndV4Se+ew9X40UXl9ufbXlQ/bxad8axibOVH7q7m7nv5xcVY75H6MRj4+K5i7NC151dz5z+0pxjLA5Xe9hGRDh0pxna/8dpq7vKvlfc797Fye/OIiJ6D5WM4ergcW3RPvb352IJyi/n9G9ZUc+dt3lKMDTy8s5o7vHZZMfboq+vn3MD2cmzn8ya+/33XYLnzPQBP1U279m62rQX2v+t0DGZqrro59o4fcJryih8AAACAhlL4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhurt9ISU0o0R8eqI2JFzvmz8sd+NiJ+PiCfHn/b2nPMnO21rrDfF4RWtCWOjc1M1d/DuoWJs5Iwl1dz0yKZy8Eh5uxERh1eVx7Vs6cFq7uXLHy/Ges9tV3M/M+fZ5WCHct1Yqzzm/iWL68k95Y23DgzX93t1Zcxf/XY51h6rbrdVOUTDZ41Uc5d8urztfNbqam7N5tfVj9/gmmuLsYHd9d9320tXFGMr/+KBau6e6+cWY0Ofr19jPXsPFGPD56+q5rbuurcaLxmbYl5ExMILz6vGa0do66vX1HMHyrFL/rhyP4mILT92bjE2d2f9vGlVfqf2/Q9Vc4Hmmc412KxJlb97cj5x4+D0cSqeV64TYIZN5hU/74+IV0zw+LtyzleO/zl5FxwAAKem94c1GADQpY6Fn5zzrRGx+wSMBQCAcdZgAMB06OYzft6aUvpWSunGlNJg6UkppetSShtTShtHhw51sTsAAGIKa7CRqL9dGwBorqkWft4TEedFxJURsS0i/qT0xJzz9TnnDTnnDb0D86e4OwAAYoprsL6Yc6LGBwCcZKZU+Mk5b885t3POYxHx3oi4enqHBQDA01mDAQDHa0qFn5TSU9sh/UhE3DU9wwEAoMQaDAA4XpNp5/7BiHhxRCxPKW2JiN+JiBenlK6MiBwRj0bEmyezs9wbcWTFxO0Kl39rtJo71j9xG/iIiJ4v3lHN3fHWFxRjK999WzV3eGm5hWLPrcvrua/dUYx98eZK6/OImHfp/mLsnP9Wbwe+4/nl9vZ9n91Xze2dX347Xu4rH4OIiDRSaZ1eyTu8vvjxBMe2W0nu3dVXzZ1zf7n1dl6ysJq784pyXXTtB+qt0fs+Uzmvrrm8mrv41ieKsbG+/nruKx8sxgYvq1/uD7zlnGJs/f/99Wpua205d/TRx6q5Nb3ryq3R8+69U97u6n+tf1bq2LfKbeb3/sQ11dzeI+UT9ujCeq396IYVxdgi7dzhtDOda7BZczK2ou40plpr7Vqs2/0yPbppjT5TuQCzrGPhJ+f8+gkevmEGxgIAwDhrMABgOnTT1QsAAACAk5jCDwAAAEBDKfwAAAAANJTCDwAAAEBDKfwAAAAANFTHrl7Taaw/4sia9oSx/luPVnP7dh8uxvKVl1ZzV/7FlzsPrqA1VI4dXVxv63jbQ+uKsYv/5yPV3Lv/cE0xNrqk3s798JlTbyfZ3rmrGMtbH68nT7GN5YFz6qfhWKVje89wfdt5dLQYS7vq7cDP+8ty7o7vWVnNXfLiq+oDq9j3svXF2OK//ko194Gbyvtd9el6K/j1H9lfjPUsmF/NHVpfbkPe20U799FHNk05t+bRH1lajZ/bf1kxtuDD9WMw9OZri7Glt9WvoUd+5qxibFE1E4BJm622253227R27920Rp8ttXF1c/xOxnPuZD0GNafbNQTTyCt+AAAAABpK4QcAAACgoRR+AAAAABpK4QcAAACgoRR+AAAAABpK4QcAAACgoRR+AAAAABqq90TuLI1EDDzemjDWOjRSzc295RpVe15ffb+dh1a07Du5GDvy+r3V3NVvL+cOX3RmNXfB3XOKsQf/y3A1t//x8n47ycP1bdeTp7bfRZtGq/G+gxOfMxER+9fWa5djZ64oxlr7DlVzt75qVTF2xru+XM1trSjv9+AL11VzF3/g9nKwpzwXEREXv21TMbb7By6o5m6/dnExtmKgfo31fu7r1XhJz/z59fiKZcVY3negmpsWLSjGzvxi/TzPG+8qxna89QXV3IVbyufz0Hnl8yIiou9gOdZ77tnV3NFNm6txgKLUzUqpC1NcN8yqbsY8W/PcjZkac6ftzta5URvXyXq+nornVU03x8C9DIq84gcAAACgoRR+AAAAABpK4QcAAACgoRR+AAAAABpK4QcAAACgoRR+AAAAABrqhLZzzz0RIwsnbne3c8Oiam5ql2PL7txf32/HkZU98aJy9rnvWVjNffBny9Ob1tVbia+54WgxdvZH91RzY/vOYqgyjR31zJtXjY8dPjyl7e4/p34a9h0qH4P2vPrRTQ8+VoyNrT+rmrtoU3m2WsuWVnPz0FB5TPXu9dG68LxirH3fg/Xk0fLGB+/aV009uqx8fPu27q7mHnnZc8u5ny23eh87VL8OOsWr9pSvk94uWp+f8b5vVOOPvP05xdiqr9a33XegfD7veGn9fF356fL5Orr18fqOAWZDNy29U4f/u8xjxz+eyZjJdtHdbLtp7aRrc9HEY9+N6nVSm8dT8Hw7WY9BNy3o4QTxih8AAACAhlL4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhuo9oXvrz9E+c3jC0J45/dXUs29uF2NHzpxfze1beFUxNuferdXc1f9ajrV/fWc1d+TxpcXYpb+9v5q75U/nFmO9n1xVzT340gXF2Hm/tKWaO3rh2cVYz/Z91dy0pxxv79lTSaxuNnZeWX5Ca+LT6d+MHThQzt26o5r75I8tKcZe8jvbq7mffcf3FGOLv72rmrvnquXF2KL7Hqzmbvm5ZxVjQ887VM1d/1/uKsZGh+sT3ffoY9V4SWtwsBofW3tmOXd3/Roa3bS5GHv8N15QzR14cfnaPnpL+fhERMzZXY7telb9lrvs7tFi7NDKVjV39/eeU4wtva2vmjv6yKZqHGBWpMoCIY+duHGcCqpzlU/cOE6Ebo59bZ66VZvnmdzvTG27aefNbOnm+DgGTKOOr/hJKd2YUtqRUrrrKY8tTSndnFJ6YPxr/V9vAAAcF2swAGA6TOatXu+PiFc87bHfiohbcs4XRMQt4z8DADB93h/WYABAlzoWfnLOt0bE09+88JqIuGn8+5si4rXTPC4AgNOaNRgAMB2m+uHOq3LO2yIixr+uLD0xpXRdSmljSmlje3/9M0YAAKia0hpsJDp8KB4A0Fgz3tUr53x9znlDznlDa1H9Q5gBAJgeT12D9cWc2R4OADBLplr42Z5SWh0RMf613h4JAIDpYA0GAByXqbZz/1hEvCEi3jn+9R8nk9RzJMXcewYmHshQPXfHVeUa1cLH6q3uBv7pq8VYuXnyMY//QLm9+Zk31Nuqr981Uoxtf+nqam5/ZUaX3VFvY73yLx8qxsYuOa+aO7Sy/D+CPSPlNvERET29lXbTlXbuo/PqbQ5zq9y2c90Lyi27IyKGXvm8YmzOzvpJd3RJeb9ffdHSau6CA18pxvJlF1dzF/3N7eVgh5aQZ32q3IZ86FsLq7k7f+aqYmzZDbdVcw/92POLsfl/V/592pXzIiKq502+YH09t+LsTzxZjef3PF6M7XrdsmruzueVz5u5W+st2bd+b6UWn+r3uflbyrk9z6vfb8auKccXfbB8LgOzYkprsFNSrZVxT/1+Wt9uh3bgaYZeED+TLei1fZ6cTvPUTevtmWqrPpOt4Ke6327m0bkKs2Iy7dw/GBG3RcRFKaUtKaU3xbHFxstTSg9ExMvHfwYAYJpYgwEA06HjK35yzq8vhF46zWMBAGCcNRgAMB1m/MOdAQAAAJgdCj8AAAAADaXwAwAAANBQCj8AAAAADaXwAwAAANBQHbt6TaecInJhj4tfsa2aO+cPlxRjrS9/p5rbc8H6Yqz9wMPV3DNuKU/RL/3+31Zz3/fYi4qxo59aU81985v+qRj7xPZnV3N/YnX5d/rbFy+u5s7PuRhLm5+o5rZ37a7Gi8q7jIiI3iOpGHvoq+dUc8/bcaAYS0Oj1dwz/7UcG73ivGpuzxfvKMbG7rq3mtuN0SVzi7GBzfuquTueu7IcvObyau78v7u9Gi/pmTevGh+96sJysDLHnYzN66/G0wXnFmOD9x2qb/wnjhRDu5fOr6b23V+ej3nb6hfK3F3tYmzXZa1q7roPbC/GylsFmGGp/Pd/5LEOuV3832Zt252222lcM6U6Vx0WWqeT2jzxH9XOm1NxHk/WaxdOEK/4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhlL4AQAAAGgohR8AAACAhjqh7dwjRYwV9jjy/lXV1M0/WW6xt+jKDdXcM/78y8VYa8WKau6TlU3/xe/+eDX3iReXGyGn9fUmye/6xKuLscF7qqlx466zirE5F41Uc/ueKLc/n3K79g4WP1pvq96/v9yKevf3DVVzd165sBhbevfhau5Yb7lV5c7L623IB+eVT5y+/UeruQ/8YvmyvOgX663g739duZ374D31MZ9zffnEeuSXL6nmzn/2tcXYsvfeVoyNHa4fg55Ky/bedeWW6xERo49sKsY2f//iau7iR8r3m3nb68dv587yOdc7p36u95Uvv0gduozuuKpcx7/gPY9Vc0fXLK1vHOAkk1rltUFERG7X11n1jXfx/6K1XO2iJ69p89jNOTVbv+9MtWzvZrsz2ZK9aeccPI1X/AAAAAA0lMIPAAAAQEMp/AAAAAA0lMIPAAAAQEMp/AAAAAA0lMIPAAAAQEMp/AAAAAA0VO8J3VuOSGMTh44sr9egln09FWMLtxyd8pDaTz5Zjed0XjH2v9/5P6q5r/vvv1mMDd5fH/OWnztSjP3lj/+vau7vb31VMbb7V86s5qbhqc/lVA0vbFXj+9eVYyv/eU41d8m39hRj7UX13Kt/c2Mxdut7n1fNPbCmr7zf88qxiIjVn8jF2PCLLq3mLnqofB2N/dCuau69V1xYjC18sJoaq27eWoyNVvJaF5avr2NPKP8+m1+5opo6f9vqYmzO3vIcR0QMLyrfb17zf36hmnvDx19WjJ1xVfl8jIjYfMFAMbb0G/Xr5PybyveyscULqrlp493FWH2mgFNKKt/b6nmVNVouLOymI7cij3W4O9X223njU889nczkHNfinTLa6rAAACAASURBVPZ7Mh6/bq4T/t1szeNsnXOd7tnZKu2UNtW/k2sqp4S7DAAAAEBDKfwAAAAANJTCDwAAAEBDKfwAAAAANJTCDwAAAEBDKfwAAAAANNQJbeeeB3IMnzc0YWxgV7mNcUTE973lK8XYxrdvqOY++FfPKcZWfrLe0nv9FeU21b/w02+t5h784XI/tb3lIUVExJIvlNsvv/W9v1zNHZ1bbvu8YOumeu4T24ux3nXnVnPbW7YVY3mk3CZ+2VfK+4yIWPaNcvvzgffsruZ+8+5yL/hL/6i+3y9cX27ZvvIbB6u5RwfL59XIgnpb7vn/9M1irDaPERGDY88txtqPLa7m7n1ZuaXgGV8+UM0dXru8GGs9+lgxlo6OVLd7+KKVxdiaz9Zbo4/deU8x9sgfXlvNPffTE9+nIiI+9/P13De997PF2Ee3XF7NXXRv+ZY81l9NjaNnLirn9tVr/ANj64ux9t3313cMnPq6aIGcWvW/03K7PTP77am3wa3ut/PGp557MrYSn4mWwRHdtdaeyfbYtW13M+aZGlOnbTftfJwlne4ZNXlM2/ST3kzd5xqkq8JPSunRiDgQEe2IGM051yswAAB0zRoMAJis6XjFz/flnHdOw3YAAJg8azAAoCOf8QMAAADQUN0WfnJEfCal9PWU0nUTPSGldF1KaWNKaWN7/6EudwcAQBznGmwkhk/w8ACAk0W3b/V6Yc758ZTSyoi4OaV0b8751qc+Ied8fURcHxExZ/1ZPhkLAKB7x7UGW5SWWoMBwGmqq1f85JwfH/+6IyL+ISKuno5BAQBQZg0GAEzWlAs/KaX5KaWF3/0+Ir4/Iu6aroEBAPBM1mAAwPHo5q1eqyLiH1JK393OB3LOn6ol9BxOsfCbAxPGFmxtV3f20c9cU4w99/+6r5o78JYzirHdl088nu/65XNuKcZ+462vq+ae/4d7ysFHtlZz97zmWcXY3vP6q7mLNo0UY6NPbK/m1ow+smnKuTXbv2/VlHOX/O6Carz1qnJts71sYTV34ebRYqxnb/3zqvZfUR7XvCfr5/qhVz+nGJv/8W9Wcze/vHxuLPtW/VX+F75tYzn4mfoxunTRjmLs/hfOKSceu38UzX14dzF24LIV1dx5d1aC9d1G3zcfKsba+/dXc2/4p5cVY3ntkWrukpeVm/Ps3jO/mrv44fLtfN5XH67mDl+xthjrvbuaCsyO416DdSP1dLhp1pPLsTw29dxOu221Krvt4l1vncZc0+n36WbbM6Uy5m7Oi47HoIv91rZdOy867TfXl28dNttpzDPUb6eb62+q251N1d+pfuy7ui/M1Dx33G/lvMqz9O7eDmt7Zs+UCz8554cj4oppHAsAAB1YgwEAx+MkLdcCAAAA0C2FHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaKhu2rkftzx/LIavPTBh7MnBelvu9/74/1uMvfFzb6rmXjw68T4jIpb85W3V3F9/zn8uxs6+ud7X8f7fLLcNXL96STV39+3lVnjn/3m51XRExMFr15ZjP3FNNXfBh79SjLUGB6u57T2V9vUVc/bV2w0eOKdcn1z9B/W5OLxvWTG26/KV1dzdl1XG9X8sruaO7S+fGyNnls/HiIg7r/5gMXbRc3+xmvve11Wuk2U/V82du6vcRj7/92pqfOnsc4qxVQvvL2933kB1u/svKV8n2157tJq7ak75XP/el3yrmvvZVZcUY8u+1FfNXff28j3l4KfWV3MX/0r5uk8vqM/VwJMHy8Gl9ftN7y1fr8aBZuuqXXuH9tipFm530R+7g25aM9fmY8babp+kujo3qi3ZO7S47qI9dldjrm23Uyv42rg6tD+vXiddtAOfsRblJ6nUW1mjdXHezOg8zlS7d23VeZpT74oGAAAAYFIUfgAAAAAaSuEHAAAAoKEUfgAAAAAaSuEHAAAAoKEUfgAAAAAaSuEHAAAAoKF6T+TO0uGe6P3GwgljCx8bq+b+0Y/+ZDE2/3cOV3N/4ENfKcb+4lOvqObG8uFiaN4XH6qmnv/J/cVY6uuv5q7P24qxPGdONXf+IweKsbE776nm9q4+oxhr795TzW0tW1rO3bW7GBtenKrb7TuYi7Fdv3RmNXfohUuKsZV3H6zmnvemLcXYpndfWM09uKZcUz2yrzymiIhnf+mXysHF5bmIiPjl//ULxdjyXfXcwbc/WIzd89kLqrnDK9rF2MrbVxZj6bEnqtudOzi3GMu7Bqq5vUfK95QvfuKKau6F/+3Lxdiun7+2mvsT95R/p/f9/jXV3DywtxhbuPloNfexH5j43hoRkdrlWETE2r8aKsZGt2yt5gKniBSRWq2JQ/31NUlNbpfv/xERMVb+uyf11pehOVdyC7/Lv8XH6uvKKWt32G5PfU1T33aHuazIlXnuRvUY9XTx/8cdjk/12KcO503tGHVzfDoZq8xHp/3Wjl+Hc70m1VK7ON8677gyF7nDNdRNbm2zne5ztXOy03Xfxbgiygdppq5rTk9e8QMAAADQUAo/AAAAAA2l8AMAAADQUAo/AAAAAA2l8AMAAADQUAo/AAAAAA2Vau0Sp9uaZy3Jb/7Q90wY2zw0WM39kaXfKMZ++8/eVM1d/aEHirH73n5eNbdnuNx+cXRJvQ3i91xxbzF23ryd1dw7960pxja///xq7tEfLreEPvTg4mruBe8v50ar3ooy91XaEW68qxjb/XP19thLf3pzMfbGs75UzT2zr9yC/o1ffmM1d8HXyq3E1/zz9mru2MJyq/GtL6kfg3M+Um6fnXvrLT33PrfcOn3w9m3V3NFHNhVj2972gmruwsfK18Lw4nJ9efDeI9Xt9m8qXyejm7dUc2vGXnRlNd7zxTumvO2jP7ChGHvxH5fbxEdE/O9PfW8xtu6jh6u5ub9Sx+/QZfTg2XOKsUUf+Eo9GZ7m9nxL7M+7Z7BnMlOxuLU8XzPv1RPG0rx59eRaq+JOraZHR8uxTq2KW5X7Wq3lc0TEWGWNlmbw9Kz9vp3maqSc23G9PkPt6zueG1Pdbof1TDfyaOXYd2irniot6qvbjaifcz1dXCedWoVPtd17N+3cO11/Ffno0fqma23Xuxlzp3mq3W+6aefe4T5XvbY7Xde1+aicyxyHmfz7YgZ85fDHY19754SDdkYAAAAANJTCDwAAAEBDKfwAAAAANJTCDwAAAEBDKfwAAAAANJTCDwAAAEBDKfwAAAAANFTKOU89OaVXRMSfR0QrIt6Xc35n7fkLF5+Vr3rhr0wY6//U16Y8jm6k5zyrGt9z2aJibMlf3Tbdw5lxPZdfXI2nrTuKsbH1a6q5+WvfLsZaK1YUY/f99nnV7fbvK9cn1737vmpue+euYuzwjz6/mvv496Ri7Mxb69fN6Nxy7q7LyrGIiCWVX2nZN/dUc3dfOViMDX7oG9XcPDxcjde0Ljq/vN0t24qxsSND1e0e+eHnlvc5VD8Gc29/sBhr76nP40xpDZaPT0R9XLVrKCKi/eST5dwL69fY2KYtxVg35wWnp9vzLbE/767f6Oja8a7B5qxfk9e84y0Txn79OZ+t7usLey4oxnrTWKehFu0cml+N51w+ja47+9Zq7t8+uaEYGx2r/7/nWGW/o7lVzd0/PFCMpVT/e2v7voXF2NHhvmpuTftwbyVYv1R/4YWfL8YuGdhazf3c/kuLsaNjlTFFxNK+Q8XY7624s5r71wdWFmMjub7fNy1+ohj7+vDRau7/3P6SYuy/rv50Nfe9u15UjG0fLp8XERF9lWtw9cC+YuzW7eW1W0REu3IdXLyk/O+FiIhz5u4uxv7m/vLaLiLiBWc/UoxtObSkmluzeM6ReryvvCbd3GG/7Vy+p+w9Mreae3S0fE85sK+eG/sr94Xapd3F39C5w32sK92sHE7FVccMzOUT7/h/YvjRLRPOxpRf8ZNSakXEuyPiByPi0oh4fUqpfIcHAKBr1mAAwPHo5q1eV0fEgznnh3PORyPibyLiNdMzLAAACqzBAIBJ66bwsyYiNj/l5y3jj/0HKaXrUkobU0obR46WX7YJAMCkHPcarH3AGgwATlfdFH4meu/YM96olnO+Pue8Iee8oa+//l5uAAA6Ou41WGuhNRgAnK66KfxsiYizn/LzWRHxeHfDAQCgA2swAGDSuin8fC0iLkgprUsp9UfET0XEx6ZnWAAAFFiDAQCT1m0791dGxJ/FsVaiN+ac39Hh+U9GxKanPLQ8InZOeQCnD/M0eeZqcszT5JmryTNXk9P0eTo357xitgfRdF2uwZp+Dk4nczV55mpyzNPkmavJMU+T1/S5Kq7Buir8dCultDHnvGHWBnCKME+TZ64mxzxNnrmaPHM1OeaJ2eYcnDxzNXnmanLM0+SZq8kxT5N3Os9VN2/1AgAAAOAkpvADAAAA0FCzXfi5fpb3f6owT5NnribHPE2euZo8czU55onZ5hycPHM1eeZqcszT5JmryTFPk3faztWsfsYPAAAAADNntl/xAwAAAMAMUfgBAAAAaKhZKfyklF6RUrovpfRgSum3ZmMMJ6uU0o0ppR0ppbue8tjSlNLNKaUHxr8OzuYYTwYppbNTSp9PKd2TUvpOSulXxx83V0+TUhpIKX01pXTn+Fz93vjj61JKt4/P1YdSSv2zPdaTQUqplVL6Zkrp4+M/m6cJpJQeTSl9O6V0R0pp4/hjrr8JpJSWpJQ+klK6d/yeda25YrZYg5VZg02ONdjkWYMdH2uwybEGmxzrr//ohBd+UkqtiHh3RPxgRFwaEa9PKV16osdxEnt/RLziaY/9VkTcknO+ICJuGf/5dDcaEW/LOV8SEddExFvGzyNz9UzDEfGSnPMVEXFlRLwipXRNRPxRRLxrfK72RMSbZnGMJ5NfjYh7nvKzeSr7vpzzlTnnDeM/u/4m9ucR8amc88URcUUcO7/MFSecNVhH7w9rsMmwBps8a7DjYw02edZgnVl/PcVsvOLn6oh4MOf8cM75aET8TUS8ZhbGcVLKOd8aEbuf9vBrIuKm8e9viojXntBBnYRyzttyzt8Y//5AHLuQ14S5eoZ8zMHxH/vG/+SIeElEfGT8cXMVESmlsyLiVRHxvvGfU5in4+H6e5qU0qKI+E8RcUNERM75aM55b5grZoc1WIU12ORYg02eNdjkWYN1zfX3FNZfzzQbhZ81EbH5KT9vGX+MslU5520Rx/6yjYiVszyek0pKaW1EPCcibg9zNaHxl87eERE7IuLmiHgoIvbmnEfHn+I6PObPIuI3I2Js/OdlYZ5KckR8JqX09ZTSdeOPuf6eaX1EPBkR/9/4y9ffl1KaH+aK2WENdvxcqxXWYJ1Zg02aNdjkWYN1Zv31NLNR+EkTPKanPFOSUloQEX8XEb+Wc94/2+M5WeWc2znnKyPirDj2P76XTPS0Ezuqk0tK6dURsSPn/PWnPjzBU0/reXqKF+acr4pjbxl5S0rpP832gE5SvRFxVUS8J+f8nIg4FKfRy4o56binMW2swSbHGqwza7DjZg3WmfXX08xG4WdLRJz9lJ/PiojHZ2Ecp5LtKaXVERHjX3fM8nhOCimlvji24PjrnPPfjz9srirGX+L4L3HsPflLUkq94yHXYcQLI+KHU0qPxrG3P7wkjv3vk3maQM758fGvOyLiH+LYYtb190xbImJLzvn28Z8/EscWIuaK2WANdvxcqxOwBjt+1mBV1mDHwRpsUqy/nmY2Cj9fi4gLxj+lvT8ifioiPjYL4ziVfCwi3jD+/Rsi4h9ncSwnhfH3/d4QEffknP/0KSFz9TQppRUppSXj38+NiJfFsffjfz4iXjf+tNN+rnLOv51zPivnvDaO3Zc+l3P+6TBPz5BSmp9SWvjd7yPi+yPirnD9PUPO+YmI2JxSumj8oZdGxN1hrpgd1mDHz7X6NNZgk2cNNjnWYJNnDTY51l/PlHI+8a+YSym9Mo5VcVsRcWPO+R0nfBAnqZTSByPixRGxPCK2R8TvRMRHI+LDEXFORDwWET+ec376hw+eVlJKL4qIL0TEt+Pf3wv89jj2HnNz9RQppcvj2IeXteJYsffDOef/llJaH8f+V2VpRHwzIn4m5zw8eyM9eaSUXhwRv5FzfrV5eqbxOfmH8R97I+IDOed3pJSWhevvGVJKV8axD6vsj4iHI+KNMX4thrniBLMGK7MGmxxrsMmzBjt+1mB11mCTZ/31H81K4QcAAACAmTcbb/UCAAAA4ARQ+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoKIUfAAAAgIZS+AEAAABoqN4TubP+3nl5YM6SCWPtM3M1t324MtQ09TGl0annzh0cqsYP7x8oB+u/blXqkJvaU992a7i88TQ6NuXtppHKRI/Vtzu8tr8YW9B3tJp74HDtGHQ4cXrL47ps4c5q6gNDE5/nERG5w34vmru3GNvRLs9FRMT2fYuLsbOW7K7mbjkwWIyl0fqYq79SX/mcah3scAwq53q7cmiP7bd8/Pp31vd7dHE53jPSYb8VudUhXinFd9xvZa56OtznUuUSbA3Vk/PQcH3jU1U9RFO/6XfMTF38hdKNGfp9Z1RhWEfaB+Lo2NBJOmgAgNNPx8JPSunGiHh1ROzIOV82/tjvRsTPR8ST4097e875k522NTBnSVxz8XUTxnb/Qf0f8Pu+sbwYy12Ur+bsqq9Na/8guvTH7q3mfvPzFxVjPSMd9lsp3nQq7MzZU/4XYG7V9zv4QPk49O86Ut9x7R+PT+wqj+nQ4epmH/3jc4qx55+1qZr7L3dcUoylTsdgSXkuvvyS91Vzf+i+Hy7Ghkb7qrn/ctlHi7F37z27mvuuf35VMfbOH/pANfc3/vUni7G+XfWLbKxS3BlbUZ7HwS/NqW63Vpjde0mHCuiZ5cLsOTfWKzCP/WD5GM3bWn+hZK0wOzxYH/PIgnJ83uP1/daKO3OfrBdX+w6X97vw7vK1GxHRvu/BcrBWREkd5rFn6rlRyU2dCjs9lW13UxSqbTc6jKs2F510mqtuFMZ1296/n7l9AgBw3CazInx/RLxigsfflXO+cvxPx6IPAAAAACdWx8JPzvnWiKi/RwQAAACAk043rwF/a0rpWymlG1NKxQ8HSSldl1LamFLaODJaf0sPAAAAANNnqoWf90TEeRFxZURsi4g/KT0x53x9znlDznlDX++8Ke4OAAAAgOM1pcJPznl7zrmdcx6LiPdGxNXTOywAAAAAujWlwk9KafVTfvyRiLhreoYDAAAAwHSZTDv3D0bEiyNieUppS0T8TkS8OKV0ZUTkiHg0It48g2MEAAAAYAo6Fn5yzq+f4OEbZmAsAAAAAEyjbrp6AQAAAHASU/gBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaKiOhZ+U0o0ppR0ppbue8tjSlNLNKaUHxr8OzuwwAQAAADhek3nFz/sj4hVPe+y3IuKWnPMFEXHL+M8AAAAAnEQ6Fn5yzrdGxO6nPfyaiLhp/PubIuK10zwuAAAAALo01c/4WZVz3hYRMf51ZemJKaXrUkobU0obR0YPT3F3AAAAAByvGf9w55zz9TnnDTnnDX2982Z6dwAAAACMm2rhZ3tKaXVExPjXHdM3JAAAAACmw1QLPx+LiDeMf/+GiPjH6RkOAAAAANNlMu3cPxgRt0XERSmlLSmlN0XEOyPi5SmlByLi5eM/AwAAAHAS6e30hJzz6wuhl07zWAAAAACYRjP+4c4AAAAAzA6FHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaCiFHwAAAICGUvgBAAAAaKjebpJTSo9GxIGIaEfEaM55w3QMCgAAAIDu/f/t3V2oZWUZB/D/w4xDNSnmJ+VYGkg5F/nBIIYhaiGjSXZRoBRIBHPjhUER1k0UeNFN2YUEg1peZCbWlHQhihl2ZY5paKk0DZbDmKOU9CGoU08Xe0mnOcfazj5z9p41vx8c9nrfs85ZDw/nPXD+Z71rzxT8DC7u7hdX4fsAAAAAsIps9QIAAAAYqVmDn05yb1U9UlXbVjqhqrZV1c6q2vna/pdnvBwAAAAA05p1q9cF3b23qk5Kcl9VPdXdDy49obu3J9meJMdsfFfPeD0AAAAApjTTHT/dvXd43ZdkR5LzVqMoAAAAAGZ30MFPVW2sqqNfP05yaZInVqswAAAAAGYzy1avk5PsqKrXv8/t3X3PqlQFAAAAwMwOOvjp7t1JzlrFWgAAAABYRd7OHQAAAGCkBD8AAAAAIyX4AQAAABgpwQ8AAADASAl+AAAAAEZK8AMAAAAwUoIfAAAAgJES/AAAAACMlOAHAAAAYKQEPwAAAAAjJfgBAAAAGCnBDwAAAMBICX4AAAAARkrwAwAAADBSgh8AAACAkRL8AAAAAIyU4AcAAABgpAQ/AAAAACMl+AEAAAAYKcEPAAAAwEgJfgAAAABGSvADAAAAMFKCHwAAAICREvwAAAAAjJTgBwAAAGCkBD8AAAAAIyX4AQAAABgpwQ8AAADASM0U/FTV1qp6uqp2VdX1q1UUAAAAALM76OCnqtYluSnJZUk2J7m6qjavVmEAAAAAzGaWO37OS7Kru3d396tJ7khy5eqUBQAAAMCsZgl+Tkny7JLxnmHuv1TVtqraWVU7X9v/8gyXAwAAAODNmCX4qRXmetlE9/bu3tLdW45a/7YZLgcAAADAmzFL8LMnyalLxpuS7J2tHAAAAABWyyzBz8NJzqiq06tqQ5Krkty9OmUBAAAAMKvqXrY7a/ovrro8yY1J1iW5tbtv+D/nv5DkD0umTkjy4kEXcOTQp+np1XT0aXp6NT29ms7Y+/Se7j5x3kUAADAxU/Az88Wrdnb3lrkVcJjQp+np1XT0aXp6NT29mo4+AQCwlmbZ6gUAAADAAhP8AAAAAIzUvIOf7XO+/uFCn6anV9PRp+np1fT0ajr6BADAmpnrM34AAAAAOHTmfccPAAAAAIeI4AcAAABgpOYS/FTV1qp6uqp2VdX186hhUVXVrVW1r6qeWDJ3XFXdV1W/G17fMc8aF0FVnVpVD1TVk1X1m6q6bpjXqwNU1Vuq6pdV9euhV18d5k+vqoeGXv2gqjbMu9ZFUFXrqurRqvrpMNanFVTVM1X1eFU9VlU7hznrbwVVdWxV3VVVTw2/sz6oVwAArJU1D36qal2Sm5JclmRzkquravNa17HAvptk6wFz1ye5v7vPSHL/MD7S7U/y+e4+M8n5Sa4dfo70arlXklzS3WclOTvJ1qo6P8nXk3xz6NVfknx2jjUukuuSPLlkrE9v7OLuPru7twxj629l30pyT3e/P8lZmfx86RUAAGtiHnf8nJdkV3fv7u5Xk9yR5Mo51LGQuvvBJH8+YPrKJLcNx7cl+fiaFrWAuvu57v7VcPy3TP6QOiV6tUxP/H0YHjV8dJJLktw1zOtVkqralOSjSW4exhV9ejOsvwNU1TFJLkxyS5J096vd/VL0CgCANTKP4OeUJM8uGe8Z5nhjJ3f3c8kk8Ehy0pzrWShVdVqSc5I8FL1a0bB96bEk+5Lcl+T3SV7q7v3DKdbhxI1JvpjkX8P4+OjTG+kkAt2AWwAAAkpJREFU91bVI1W1bZiz/pZ7b5IXknxn2EJ4c1VtjF4BALBG5hH81Apz3lOeg1JVb0/ywySf6+6/zrueRdXd/+zus5NsyuSuuzNXOm1tq1osVXVFkn3d/cjS6RVOPaL7tMQF3X1uJtt2r62qC+dd0IJan+TcJN/u7nOS/CO2dQEAsIbmEfzsSXLqkvGmJHvnUMfh5PmqemeSDK/75lzPQqiqozIJfb7X3T8apvXqfxi2mPw8k+ciHVtV64dPWYfJBUk+VlXPZLIF9ZJM7gDSpxV0997hdV+SHZkEitbfcnuS7Onuh4bxXZkEQXoFAMCamEfw83CSM4Z3ytmQ5Kokd8+hjsPJ3UmuGY6vSfKTOdayEIZnr9yS5Mnu/saST+nVAarqxKo6djh+a5KPZPJMpAeSfGI47YjvVXd/qbs3dfdpmfxe+ll3fyr6tExVbayqo18/TnJpkidi/S3T3X9K8mxVvW+Y+nCS30avAABYI9W99rsWquryTP6Tvi7Jrd19w5oXsaCq6vtJLkpyQpLnk3wlyY+T3Jnk3Un+mOST3X3gA6CPKFX1oSS/SPJ4/vM8li9n8pwfvVqiqj6QycNj12US9t7Z3V+rqvdmcmfLcUkeTfLp7n5lfpUujqq6KMkXuvsKfVpu6MmOYbg+ye3dfUNVHR/rb5mqOjuTB4ZvSLI7yWcyrMXoFQAAh9hcgh8AAAAADr15bPUCAAAAYA0IfgAAAABGSvADAAAAMFKCHwAAAICREvwAAAAAjJTgBwAAAGCkBD8AAAAAI/VvY1/q87vD2MMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00087484 0.00123934 0.00109112 0.00110232 0.00110026 0.00104614\n",
+      " 0.00104037 0.00129254 0.00109223 0.00107844]\n",
+      "[5.40359179e-05 9.75274597e-05 9.07310678e-05 1.00441815e-04\n",
+      " 8.54831815e-05 8.01334609e-05 8.96128258e-05 9.79491888e-05\n",
+      " 8.51993245e-05 9.00630184e-05]\n",
+      "-6.857604026794434\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00087636 0.00145732 0.00126547 0.00136442 0.00137865 0.00118059\n",
+      " 0.00101449 0.00142372 0.00114152 0.00125633]\n",
+      "[5.88757903e-05 1.00565449e-04 1.25849489e-04 1.41201701e-04\n",
+      " 1.14368711e-04 8.88992436e-05 8.21924041e-05 1.01638565e-04\n",
+      " 8.24286617e-05 9.62268532e-05]\n",
+      "-6.5366106033325195\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00090084 0.00082613 0.00080157 0.00079006 0.00078491 0.00078284\n",
+      " 0.00078192 0.00083119 0.00110899 0.00134836]\n",
+      "[5.0890656e-05 4.8570320e-05 4.7838235e-05 4.7390033e-05 4.7154710e-05\n",
+      " 4.7047048e-05 4.9427596e-05 5.6197128e-05 8.1184968e-05 1.3125903e-04]\n",
+      "-8.302518844604492\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00093555 0.00149001 0.00143904 0.00139482 0.00143191 0.00121798\n",
+      " 0.00152989 0.00182905 0.00160298 0.00147049]\n",
+      "[6.3019113e-05 1.0388036e-04 1.0942226e-04 1.1467687e-04 1.0187882e-04\n",
+      " 9.9135053e-05 1.4488670e-04 1.3994958e-04 1.2806655e-04 1.4978170e-04]\n",
+      "-7.998033046722412\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00093596 0.00113938 0.00140085 0.00107109 0.00145253 0.00127409\n",
+      " 0.00131427 0.00129293 0.00137934 0.00129277]\n",
+      "[5.7595818e-05 7.9879013e-05 9.9836681e-05 7.4627860e-05 1.0210961e-04\n",
+      " 9.6425902e-05 9.0297181e-05 1.0062425e-04 9.2640577e-05 1.0039489e-04]\n",
+      "-8.839351654052734\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00095637 0.00107736 0.00140452 0.00123233 0.00136053 0.00133402\n",
+      " 0.00131263 0.00107254 0.00130155 0.00122698]\n",
+      "[5.6429119e-05 7.6098775e-05 9.3793642e-05 9.3659764e-05 1.0391871e-04\n",
+      " 9.3777642e-05 9.9660530e-05 7.5695141e-05 8.9184112e-05 8.8543224e-05]\n",
+      "-8.661943435668945\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.00099278 0.00091123 0.00088582 0.0008749  0.00087474 0.00090352\n",
+      " 0.00087659 0.00087496 0.00087637 0.00088615]\n",
+      "[5.6349530e-05 5.4012788e-05 5.3447886e-05 5.3075277e-05 5.5162396e-05\n",
+      " 5.5498822e-05 5.3237185e-05 5.2952939e-05 5.3693253e-05 5.4321838e-05]\n",
+      "-7.037334442138672\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "CUDA out of memory. Tried to allocate 218.00 MiB (GPU 0; 10.92 GiB total capacity; 9.64 GiB already allocated; 175.50 MiB free; 589.61 MiB cached)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-77163f0ed854>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0muse_GMM\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, tier_num)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mtier_num\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tier_num should be larger than 0, got %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtier_num\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtiers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtier_num\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/tier.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mh_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mtheta_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mW_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_f\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/TTS/MelNet/model/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_h_t, input_h_f, input_h_c)\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mh_f_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh_f_sum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mh_f_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_delay_RNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_f_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0mh_f_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh_f_temp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mh_f_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh_f_temp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [B, M, T, D]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    557\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_packed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward_tensor\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    537\u001b[0m         \u001b[0munsorted_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msorted_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute_hidden\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munsorted_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/LYH/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward_impl\u001b[0;34m(self, input, hx, batch_sizes, max_batch_size, sorted_indices)\u001b[0m\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m             result = _VF.lstm(input, hx, self._get_flat_weights(), self.bias, self.num_layers,\n\u001b[0;32m--> 522\u001b[0;31m                               self.dropout, self.training, self.bidirectional, self.batch_first)\n\u001b[0m\u001b[1;32m    523\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             result = _VF.lstm(input, batch_sizes, hx, self._get_flat_weights(), self.bias,\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 218.00 MiB (GPU 0; 10.92 GiB total capacity; 9.64 GiB already allocated; 175.50 MiB free; 589.61 MiB cached)"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training Start!!!\")\n",
+    "iteration=1\n",
+    "for epoch in range(hp.epochs):\n",
+    "    for tiers in dataloader:\n",
+    "        \n",
+    "        lr_scheduling(optimizer, iteration, 3e-4, 10)\n",
+    "        \n",
+    "        model.zero_grad()\n",
+    "        mu, std, pi = model(tiers[1].to(device), 1)\n",
+    "\n",
+    "        if use_GMM:\n",
+    "            loss = criterion(tiers[1].to(device), mu, std, pi)\n",
+    "        else:\n",
+    "            loss = criterion(tiers[1].to(device), mu[:,:,:,0])\n",
+    "            \n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        iteration += 1\n",
+    "        \n",
+    "        if iteration%100==0:\n",
+    "            fig = plt.figure(figsize=(20,10))\n",
+    "            ax1, ax2, ax3 = fig.add_subplot(221), fig.add_subplot(222), fig.add_subplot(223)\n",
+    "            ax1.imshow(tiers[1][0].numpy(), origin='lower')\n",
+    "            ax2.imshow(mu[0,:,:,0].cpu().detach().numpy(), origin='lower')\n",
+    "            ax3.imshow(np.tile(mu[0,:,:,0][0:1].cpu().detach().numpy(), (16,1)), origin='lower')\n",
+    "            plt.show()\n",
+    "            print(mu[0,:,:,0][0].cpu().detach().numpy()[:10])\n",
+    "            print(mu[0,:,:,0][1].cpu().detach().numpy()[:10])\n",
+    "            print(loss.item())\n",
+    "    break"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:LYH] *",
+   "language": "python",
+   "name": "conda-env-LYH-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tier2.ipynb b/tier2.ipynb
new file mode 100644
index 0000000..7ec97bb
--- /dev/null
+++ b/tier2.ipynb
@@ -0,0 +1,847 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "from model.model import MelNet\n",
+    "from model.loss import GMMLoss\n",
+    "from torch.utils.data import DataLoader\n",
+    "from utils.data import MelData, MelCollate\n",
+    "from utils.utils import *\n",
+    "import utils.hparams as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "use_GMM=True\n",
+    "dataset = MelData(hp)\n",
+    "collate_fn = MelCollate(hp)\n",
+    "dataloader = DataLoader(dataset, batch_size = hp.batch_size, shuffle=True, drop_last=True, collate_fn = collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda:1')\n",
+    "model = MelNet(hp).to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)\n",
+    "criterion = GMMLoss() if use_GMM else nn.L1Loss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Start!!!\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.3468229  0.34313345 0.3198754  0.2690809  0.33437732 0.25497806\n",
+      " 0.30199602 0.32684138 0.2687945  0.31004634]\n",
+      "[0.3929273  0.43327954 0.43674943 0.3899401  0.43195835 0.28432602\n",
+      " 0.34378827 0.43377006 0.39711425 0.42324716]\n",
+      "-7.922926902770996\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.17763384 0.17037089 0.146782   0.17661749 0.28591847 0.26044887\n",
+      " 0.22498995 0.20631005 0.24399868 0.27290037]\n",
+      "[0.16109926 0.16351265 0.12128195 0.25970423 0.4114057  0.41873562\n",
+      " 0.3947398  0.31668252 0.37988526 0.37112   ]\n",
+      "-6.800063610076904\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zQany7rTXp69rcrWXlyinz50ouuOkJ4Jjerl7siP6Plsv91G5xd27dEYff2LQXUvLaX2Mcrt3v6phHh/vWsLPhvce2Jwe11Wz7vxh+XZVMt4/ZrXBb85nkf8nzpZqbiMA86DBn1FW/AAAAAAAAMTUGSd+jDFrjDEPGGO2G2OeM8Z8rPb4MmPM/caYXbV/91z4ywUAAFgaGIMBAID50EyoV1lE/oO19nFjTIeIPGaMuV9EflVEtlhrP2mM+biIfFxEfu+MR0vUls6eRWlvP7zr1Cv0JVdyrt214mrVt+zJ4ahtM0EJSq9tbnDPK7foubD8OjeWainpJcG24ELQGpWZz/flVFfqTddH7czpKdVXfWq7zKVltw4PO/jz66L21Mpg6XXZLRG99H597mrFvfIjP9Om+rLDrkx7/9265HzHHlfmvjLs7qu5/iq139had76qXoEs2dvc605tmTvU7kzsa16ltpOj+aidOK1D8eypoXM6BwAAF4n5G4MZF8ZV6tBf0GkvvKvcqkt9Tw54ZcCNDqnKjLux3Pg60X1jXkhVQR+zwysDXu5044bJlWE5d7c9PaDHOhPD7hiZ4RbVl/KPH4RpldrctYQhb2nveaboxnipvC6vPbHCG1MGUTnllS6k3g+vEhH1c2t+mT5mouA6Ow7rMfLkcu+6vNuQHtVjvPxyd8xyEDaXmnbvXUuQIiCd8a7TBqXEvROWu/V9rnhhZ6ain5cpuDGz8ULvbJgkwC9dbs4iEIGwMAA4K2f8C2utPWqtfbzWHheR7SKySkTuEJF7a7vdKyLvuVAXCQAAsNQwBgMAAPPhrHL8GGPWi8i1IrJVRAastUdFZgYmIrK8znM+bIzZZozZVpLCXLsAAACggfMdgxUr0wt1qQAA4CLT9MSPMaZdRP5WRH7LWjvW7POstXdZa2+w1t6QluyZnwAAAIDIfIzBMsmWMz8BAADEUlPl3I0xaZkZcHzJWvvN2sPHjTGD1tqjxphBETnR1BlruX2Kb7tBPZz5wbaoXbz91arv6K1ebPB6nRNn/Z0uTtmUdbzvkTcti9qtx3VfpzudJA4c9w6oy7mfuMFNVq050ar6pld1RO22J3Spcj8bUHa4qPr8+OlqS5AIp47iml61nfR+uOvcpWO1x9e7trlEB91n9rtcQQOZAX1Z3mFsOchnVKdcvX3sObVdeM2tUbsShLZ3zEM190KPPmj+EjeQ7TygB7XJnz4jAAC8nM3fGMyIJGZ+7yt26Zw7rV4Z82KXHhpO97vBQSWnn9dywvUVV5ZEc+ObzKjusd75pgfc93olmJsqdbjxUqVNj+OqXt6YMM+OnyvGBiNdP+dPohw8MZGYs11N6f38Y5ZbdV9XtxunFru79OG9WxSOkRLesCs1rV/rdK+7llJb/VLf5Vav9Lp+q2S6zyv1fjzI4+PnOjL1jz89EOQU6nHHDPMgpUe8F+i9VyYoJW8b/QRdtfX7ziYf0LkghxCAmGmmqpcRkbtFZLu19lNe17dF5AO19gdE5L75vzwAAICliTEYAACYD82s+HmNiPyyiDxjjHmy9th/FJFPisjfGGM+KCIvisi/vDCXCAAAsCQxBgMAAOftjBM/1tqfyOxFtC+57VxPPLlChzj5K15PXKv7yl2u9GNuuw63Svz4waidvPIy1dez0x21dc+w6vOLSZY3rXTnvl4vgiqvdOXC8092qL5ip1u6mj12XOopduvX03JoImonTutQfR1g5fghWiIiPS0rovb4Kn387Ej9Zbq2w92/7LEJ1Vfsbwt3d7xy9ade1R61++56SO02PeCFsQXLjDPD7l42WLzbUL5HH7TkfRwS00F4Wrne3QQA4OI3r2MwI1EYT3a4/vdjWJbbDx+q6EgfSXsl29NtOqy9mvXGJuEr8MrH+yXVyy363JUWL9wmWwn63BA2UWoQlhMMOKrp+mMk8ULQ/FCvMPQq6ZVeLwfhaamk27fYFiys96/F6AsrtTcYu3ldfuoCm6q/cL/cKDQuGZzLD+9KhNfsrnNyQPcVetzzWvQwVSptbgye8t/vij6GH/plw1Lyfghao7CvC+FCh5IBwAVR/7uEv2oAAAAAAAAxxcQPAAAAAABATDHxAwAAAAAAEFNNlXO/EHrufahu3+o/eVBtn/7QLVG71B7u7Uxu7Fbb7dsORO1ygxw8+eWuZHvHPt035MUzD23WuXRW/E99nfXYoDSm9eKnbWsu3H1O5YOH1HblGpeXaGSz3reac/HS+UF9w3JPuZrq5ctWqb4pr6SqzmYkknh2T9Tue8SVKjU3XF3/ooMQw+TwpLvGNp1PqDo5Kc1oO6pzCBz4WfeemKp+rQOjl0btyo7dTR0fAIBYMkZsembYV2rX+fJyXv6XUmuQ67DTy60TfK9PrHPPS6WCnDIurZ/khsNEO2473+0OWs3q/WyrO3fmkK5/nvWOaQpBKXmvFHeYO8cm3fNyOv2jyplj/PwyYe4ZbzMsm55JufxJE3363MZ73cmC7it1eqXYw5Lq3mbeK+2eHdPj0mqb914l9TVXs9573K6H/2m/3Ho4ZvXa5aCUfLHLez3FoK/TXVvKzy9kgvxS3ph4Vql3/74nGuRmCi10PiAAeBlgxQ8AAAAAAEBMMfEDAAAAAAAQU4sW6tVIsneZfsBbsTk1qJdvnvjIrVF7Yp3u27S3y200CPXqePqEO/5tg6rvyj844i6jWwdA6cKi9YWlUe0Tz0Xt8mtepffd2dwx/dKlmVH9NmaucGuXcwd0aJQd6I3aqSEdXtXx0yfrns+scuXjR29aHrVbj+nl1csfc9c1tk6vfz75endvu/b0qL7Ej5+oe27f4Tfopd6ZYW+JePBpLvW70K/EjqYODwBALFkjYnMzX5TTvfp3v3YvDGhyVRCe1O5Cc5JBOFfqQKt73olW1dfihTKlpoIRUxg69dLDCf24SbvzJSr6uqp+KNOs0CgvpD4cG3S4ffPdQcjbSTfGSEy4WLVkQb9uvxJ7oUdfc6fXLnaqLjFlr/x5MCytZL39gnHjlDc0LXa7e5meCOLM0t6Yr6rvScHLhjDdq29KW8pt2zCkyru34Wste9dSqOhrKfS47daEF0qW1PvZivfZSNQv9R6aVfrddzZhYeeLsDIALxOs+AEAAAAAAIgpJn4AAAAAAABiiokfAAAAAACAmFq0HD9Dv3aL2l72RVfe/dS7dH3yrvcfjtpT/7Ra9S1/dNy1P/OM6rNXX97UtZT37o/amRtXqL7jb18XtfvueVQf38vPYxrkx2k5OK62p955ozt3i45DblCtXin0uLdu5YPTuvPNLnfP+NUrVVfuZCFqZ3YfVX0m7WLbbUnnBjr8TndfBn/iXk9i3xG139D7g9ryHr+kqk2dW/x1y3H9vI5DLjY8UdJx1smtz7vzndPZAACIB5tMSKlzJpFMy+kgd4qXWyV3Wn9jTk648UaiN6/6/JwvJsjBk/CrwIf5WLy8MbkR7xhBXppq2V1XoqC6JDvuHbPS4Fs+6Cp2uwcShbA8uRsHJYdcLpow7431fjb1c/OIiAxPtkTt3CndV/DSG9rgp1c/F1GYU8h6p09NuSempoOcSONerp6M7qvk3PZ0fzAG83PrmETdPhN+bNpdnsfqqM7dU/XHeYk6bZHmE2YG/LLzDfP9XGgLmU8IAM4DK34AAAAAAABiiokfAAAAAACAmFrQUC+TzUpy/SUiokO7Ql17gqXE/8uFGSWC6C3jlTUf/mUdPta7LVhjW0fy0g1Re3ytngtLeNXKd376etW36d9uber4Y1d2q+2p5e4cyYJentq9wYWWSdGdvHLqtNqv46sPR+3Jn7tJH//rLhyu/1v6Pldf68LTKqeHVV8Y3uUbeGQqah98sytrv+bvdWjcqr87FLVPvHGV6vOXfWd3HlN9ZWmOCZYED13hlhZ37tdrkNOvvTpqJx94vMkzAAAQQwmRctvMd2apTY91ym3pqF3srB+60t6qx2fFy9y398SwLude8UrEV9NBqFTaDT+r3kjUJvWYKH3SXVfrsSCcp8ly3olggOGPIwrLdN90n7uYlkN+iXO9X9lFc0m1RY89chkv/Cm8ld4l54b08/zQ/0RF9xVXuBeRbHXt8o6c2s8fs1ayQQl6bzM9fhb3Ul1j8ID3tPA+Nx1j74eZVSr1+xqUdvfDvs7GooaIAcACY8UPAAAAAABATDHxAwAAAAAAEFMLW9XLWjGlmbWgh/7jrapr9R8/GLWP/rYOOao86daWrn4gqGD17K6o2f1EEKp02SVNXVZ1/8Go3XZkQPV1b3cVrFZ8+rmmjhfqekaHaaUudWUdhjbrt6C870BTx0wNuhCr3OmS6jv182478dAVqs9fKNsotCvkVy1b/VOv49qr1H5j1w26cwcrdlPT7uzlw7oaWLNaT+qD5vu9peRJvdQ3c3g0ap9j0QgAAGKhkjYyOTDznVnsCr4vx11I1dSgDqnJeNWtxrp1OJfZ78ZnLdP6mCkXIS6JYlB9yvrVwPwOfc2lZS5+yCb1eKnU6v12mQorUblrDsOT/LCtcpt+rVMrXGd3xgtHywShcX5YVl5fdKnizp0Kqo1VvGsJK4X5lU+rSX2+9h3u/almXNsm9PEzQ+55BV1kS1JT7vjpySBsqsmQJ5vS+1kvls2U9espeFkOjBeyZcOqYf7INAzZ8q8r0eC36gZhYI2ca4gYAFysGv1VY8UPAAAAAABATDHxAwAAAAAAEFNM/AAAAAAAAMTUgub4qeZSMr2pX0RE1n5nSPVVXuPKjE+eSqu+zd93eXZOXN+u+lYcc6XLj7w9LC1+OGqbIBeNfcLl60muXhm1p3v1XFh6nTvf4V+5WfVt+tjD0gyb1oHWqUkX0D49oPuqr7s2aid+/ETdYxYvc7l0TBCb3fc3Lga/+pQuOV+448aoHVblbGTPl9370/ddVz6096c6V09H3uUNGrq+V/X591a/i83LL9PvT8sJ99pTeR3jbVuz53gWAADixYgr6T2xXme+S+bdWCSzfkz15Y+3Re2BnnHVd6LisgmUxzKqL+3lmym36u/uqpc/x09IYIMS5OnTc5d9F9Hlw22Qq8U0KOfulxk3ywuqqzDhxjfVnDtheP2ZUXeQ8U36mvN5N4ZtydXPWZMo67Fb63Ev71FV9xW7/ZxI7pg2yONTbnP72bQ+Rr7PXWfHgeC6Ut6BgvxCxkuKZDP6mOmcu7kmSKZomqyU7ufZsWEeHz93T6M8RI3y/4TOMR8QALzcseIHAAAAAAAgppj4AQAAAAAAiKkFDfUq9lo5+Kszy0K7HuhRfe2H3HLRrmd1qJd99NGoba67RfVVdu2N2gNeW0Rk7L03Re22v3us7nVV211oVNsxvQS01OqWoIahXckrL3PX8fzOusefWtepticGvRLkwTLjoSvdMuO+H3vn6tNhU8mn3GuduvUy1df90Rfd9X9DX0vuhHe+G1+h+o7d2hG1V/z5g6rv0k+7Nbxjl7jHq526tGtifDpqT/cFYXMTTa77bSA1rbdzw+66pvr0mudylwv1YoYTALCUlbMiI5tn2jYZhBJ1uXalEoQ1nXbbp3o6dN9uFzQeHtMvmx6WLpek2/bHWYmcjsuqrHLb+UkdoJ70hjPVVj1uTKbc8LbYFZRU9yLSOjumVJ895r+e+kVxy21eGfOS3q+3azJqj7YFge3ePammwzAwrxmcu9LihXAtz0ftqWoQtO+Xd28vzXXpM8dIBKHwyeTcbRGRihsX2xYdz7W6dyRq79uo34PiMre9MuedLwy1chkCxFT08VXo19mEaM1XWBgAvNyY+t9d/PUDAAAAAACIqTNO/BhjvmiMOWGMedZ7bJkx5n5jzK7av3saHQMAAABnhzEYAACYD82s+LlHRG4PHvu4iGyx1m4SkS21bQAAAMyfe4QxGAAAOE9nzPFjrf1nY8z64OE7ROQNtfa9IvIjEfm9M55sLCHd98/EI9/ykW2q7/FPXhe1xzfqON7CH97qNsIKlOvWRO3qydOqLz0e1vCcm/VKdp68Xp9g4+8+FLXDPDuN8vr4skNFtX38BpcXxxZ1LHXf5x6SOQUx1+VNq+feT0RO/NX6qJ1531rV13LMBcUnx3X8d5jXx5d48XjU7npuImqbdavUfvvf767Eq2QAACAASURBVLZbj+kY66kV7t4m+/tVX+Xkybrn9uV79PuTHXXtsHToqVe42Pfl/9TU4QEAuGjM5xhMEiKV3MwXZcdKXZZ9eqw7apfHdVn2lJeeJcycUup047VEIchLk3N7F9t1XzXrxl0lL19OZ6dO5Df5nFvMVOrQZ8+d8q4rzCGkSoTrLj+3YntWj89OeykZy21enqA2fZBE0SvL3qdzNU4W3P0rtQd5j7yhXHhPEhW3byWrz9dyzG2XR93YJhz3VFa5/D+5Fj3Ga/Fea6Erp/qsV87dBDlwrFfePZnVOXgGW8eidnadHnNvL690x0i7e9no+GL1+N/41dwb5eYJ8/80yHEx7xrlEwKAi8i55vgZsNYeFRGp/Xv5/F0SAAAA6mAMBgAAzsoFT+5sjPmwMWabMWZbOT955icAAADgvPljsMoEYzAAAJaqcy3nftwYM2itPWqMGRSRE/V2tNbeJSJ3iYh0tq20vc/MLC/e9TN6KXHiLW6p5OWf3K/6ysdcmNHpD+ly7uUDB6P2xP9zs+rremCP2+jRuQ9N1p3fTrvlsO379fLQib/fGLVPPTag+pY9566588u61LuvmgmWCF/llsZ2/0iXej/2Wy6szQ+9qmxYofZLHx2O2slJXVL94Fvdku1Lf2urvpbXXRu1zckhfaH+0thg6aodWOY2xl2oV7VdLxde+213zENvX6b6yq3umCYVlAtt0tRKfV03/9IzUfsfd16u+jZ+luW3AIDYOacxWG7VGpsem/meHz/dpnfs8kJ4bBCC5EUMJTM6nKe6xk0mlUt6SGlP1Y8RK7e4MYAf/hSGXo0Muu10UOp9atq9hs4D+tyZjC4t7uvrc2Fum7v1rfvHTS6kf/qgO2YlqH5eWObuUWVKn3vjqqNR+9l0l+pLTtUPQfLDmsJS7/k+L7TML38e3NfqpHvdnb06nK8t4554WA/PRLxQLEkF/2tQdp+NdPD+T5W982Xyqi8x7h2n2XAoE/wenXA3xQTRXNY/5nyUaD+bcvG+hQwrA4DzcK5/Kb8tIh+otT8gIvfNz+UAAACgAcZgAADgrDRTzv0rIvKQiGw2xhwyxnxQRD4pIm8xxuwSkbfUtgEAADBPGIMBAID50ExVr/fX6bptnq8FAAAANYzBAADAfDjXHD/npNSZlMNvmslps7q4RvVlT7nY4+mrdanyXFdH1E7o6pRK+9/oPDvHfsPlAxp4eFT1VZ58PmqbwVdE7dEbdVnOjjv7ovZgScc2Z7/3aP2L8VTTemGVedzl9Wl95zHV1/2bLsbYXrrBXe/DT6v9/CvZ82c675FNu2NU3nCd6kuPuBhsk9F5lhrFYF9y976oXRUXz/zUn+r8Qu1fdzmFcp+8TPX1evHlxw9s0H2f1/ehnpfyE7zkkb/2chat09efX+7ug75KAACWFpu2UhyYGT2YKZ1nz6a8HHxpneukuMzleGlP6r7SHpfDJj2pv58Ll7rxRinIB1jqcMPPqRXu3Gtyupx7erU79/5DfaovsdHtm9+lk/C0Zl3umUpWjw26cu66VueGVV/KyyNU7HDHCHPu+Hl2kq16bDjQ4vI4PtWj+5J5d8x8UIstmXfnSE8GuSHXu1xK/lCt+qLO1ZSYds+7pu+w6uvPuPyMX1qhc1ZWc+79SE7r/zUwZfcarhjQY7VL2k9F7cPT3apP/DL3fl7HMIdQo9w6fvX4RFDq3RuLSvX8czo2LBcPADHAXzkAAAAAAICYYuIHAAAAAAAgphY01CvVWZK+t8wsPd11ja4luenDO6N26Q1Xqr5d/7o/anftbv58yx91y20TIxOq76RXFr71lFtL2tIxqfZrue+5qF2+7frmT+5JTeqlvtOb3XLV7H16uW373oeidrLX3aPkpo1qv6Eb3RrhNf+o499syi1/nVitw7k6S+7cL759rb7OvNteeddTqu+5/3RF1M73eiVOW/Xy5xP3uZLq1XKw7LvcErW7Tp9b2czpNfpeGuuuZfUDQZnZDCU2AQAQmSnFvXLtaRERyaX09+W+Fwaj9vLlOjQ+6YXYrOkYUX3bplyIVWFEjzdSXunvyTU6FMdUXOhPudPtd/OyfWq///PCq6N2S4cOxc8fCUrSqxO473+b1ufOJt35BtL6tXZ3TkXtkXVuzJIZU7tJYZm7J53tOjzt6dMr3TV36hLn00X3e2uuVz9vctiFwyWndTn61pyLLetrd+PUfRUdsjfY617PK9sPqb7epBsHm5aK6rPppNcOQr1KbvsVXQdV39rM6ajdk5pSfU/kXNoG//gmpa/ZVN3xGwZs2WBM54d36UOK2LMfY6rQsQtlHkLSAKChBn/KWPEDAAAAAAAQU0z8AAAAAAAAxBQTPwAAAAAAADG1oDl+EgcTkvudmZjsZa/SpT2nX+/y+oxsDGKbj7v26Zt0PpvTN7n471VrTqu+rt8Yj9q7P7RK9a3/Ty6XzqFP3Bq1l/2fdrXfr+xw8cyvzv1U9b33s78Ttdd86jHVZwsuFr3Urm/zqv/rgpHX/c521XfiyWvcMSbdMapPv6D269pzIGof/+hNqm/qVhfH3fqgfj2de1188aof6XxG5iGX1yeMjs6ednHqmR88K/XsusFdS6pfx69/8Cp3z//qmreovnXfrHtI5ZZX7FLbW9vXR+0XN+ugxlX36c8RAABLlTEut89ty3eovi1e+z2DOsff8VJn1L6lXX8HX9Huyns/Nz6o+vYM90bt6uai6iufcDkMV250JcFXZ4bUfrdf4sZIL4zqnIhDLW6MNH60X/V1v+DyDdkgn81Azo0NO5I6B0971h1z1BsITa/UxzAlN97oadVjnV9b48aKf3fqGtW3dcTla0yndZ4lOeLGLCU9dBNbdH0HTvZE7URSX9frB1wiTD+nj4jImrQbI1+2+rjqq2Rd3shEmOPH2+5J6XHjC9PuPT8wpXN3GlMnn40xdbdN0GeTXvKeMD+O8d4gG54rTPpzDi5EPp55uCwAaKx+kh9W/AAAAAAAAMQUEz8AAAAAAAAxtaChXvnepOz8lZklwxu/pZfXHvhZF/rVsVcvryy0uSVLrXt1+E7/ky70q/XhYdW37/MuvKvzu/pahn7NlXPPDrvz/fmn/pfa731bfjNqr9iib1enFxBlNm1QffZZF5o1cokucepPtz1+ZLXqmv6Qe31X/He3TLf0Zl1K/sjr3TH/1Xt+qPoePO2WEh9O6PXCiaJbFnz8pk7Vt3KfW0ZdPqaXAe/4dVfW9PKyC8s7+vputd+Gb7ll0rf/hQ6N+8tH3hi1O/Vb1bRtB9eo7c0f3Ru1x958ueprOao/YwAALFXZRFnWts98+T45psceKS9sZvuUDtk6NOW+529o0+XWtxzbHLVHp3UI/y9esi1qvzC5QvX90/quqP3W/v1R+3Ute9V+GzMnovZ9cp3qW+GVnf/c6OtUX2mrKzN/2YZjqq8v68ZWl6RPqL47Vrowt69d68Z8xbKO0cl7oVd/uPHb+rqSLhxqZ7t+3ftXunCo6/p1ufXvn3Ll6dMn9Vj3XRufi9pbT66P2tmUDhfbP+XC636552HVl/RCrxJBGFY54YUGpPRvwtYrv74sCB9LZt1xnhvVn5vp027cKMY9zyb18Y3aDsLMKl4oW3DNNizvXs+5hmzx0zjqsWFCDOAiQjl3AAAAAACApYeJHwAAAAAAgJhi4gcAAAAAACCmFjTHT6qlLP2Xz5TtrLTo3DAb/8CVQz/4OzeovsyYa696n44vL9/vlQsdH1d9qz/l4pIv/4ttqu+JP3ax4h3/1pVs//kHf13td9kXXAlSa3Qp+dTIVNSubNclTn25YR0LOnSVC75ry+hjVg93RO2Ra1150lPv0eVCEztd/Pr/vu+Nqm/NFpdnZ/qtOrZ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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.2700621  0.18777898 0.19672291 0.18874723 0.2352103  0.23225968\n",
+      " 0.17503062 0.17125437 0.18039452 0.1880991 ]\n",
+      "[0.3264986  0.22807065 0.22396143 0.22578567 0.32325047 0.32427138\n",
+      " 0.20638543 0.1993503  0.27467838 0.35635692]\n",
+      "-9.368375778198242\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.2111669  0.23694775 0.17575924 0.16327584 0.1918169  0.21903221\n",
+      " 0.21824396 0.21321383 0.21271771 0.16418073]\n",
+      "[0.27817914 0.2709618  0.20969935 0.20552455 0.27850893 0.2928044\n",
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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.23840527 0.25716224 0.22653453 0.16860087 0.16444163 0.16484693\n",
+      " 0.17179506 0.1263066  0.16265722 0.1522769 ]\n",
+      "[0.37503865 0.3996928  0.3760733  0.23639531 0.22053815 0.2204308\n",
+      " 0.2279901  0.13913015 0.25069666 0.21999565]\n",
+      "-6.503689765930176\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.13728622 0.10655394 0.10968526 0.1172573  0.11275986 0.1149533\n",
+      " 0.21425574 0.21541022 0.23582849 0.22709455]\n",
+      "[0.15137087 0.10601314 0.11722241 0.14365637 0.11338869 0.10551701\n",
+      " 0.36016425 0.38243216 0.3877844  0.3564984 ]\n",
+      "-7.371954917907715\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.13392738 0.15679039 0.1373042  0.1454273  0.14624007 0.11538179\n",
+      " 0.18445477 0.23255827 0.23491895 0.15145692]\n",
+      "[0.14281422 0.20080833 0.17861801 0.18605544 0.16986223 0.11870668\n",
+      " 0.30121964 0.41188744 0.42241654 0.28951117]\n",
+      "-8.08214282989502\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.2049005  0.18462455 0.17842524 0.15941463 0.21694103 0.18168719\n",
+      " 0.15347326 0.16764058 0.16598636 0.16556433]\n",
+      "[0.3838226  0.39726064 0.39674577 0.315894   0.36059627 0.27986458\n",
+      " 0.21211237 0.30877572 0.30457938 0.33101797]\n",
+      "-6.888953685760498\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.22663666 0.23782206 0.18467367 0.18074346 0.2067683  0.25855657\n",
+      " 0.24486539 0.22317055 0.21855427 0.2512727 ]\n",
+      "[0.3210761  0.38862523 0.38440412 0.38834512 0.37675226 0.4088729\n",
+      " 0.3995931  0.39749226 0.39821094 0.39592892]\n",
+      "-7.591490268707275\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.23284449 0.18735375 0.17664048 0.18718976 0.16760768 0.22060585\n",
+      " 0.18552092 0.16247371 0.16181293 0.16049527]\n",
+      "[0.3798721  0.3755679  0.36643466 0.38868567 0.34834656 0.3936467\n",
+      " 0.3587009  0.25110078 0.2969488  0.28645492]\n",
+      "-7.523237705230713\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6YfwlMQabjk4dg3Xq+EtiDDYTZ2UMxkTE/He9pGdjjNtjjFOSPinpljnuU8eJMX5T0rGX/PoWSXe2/n2npDfn2qmWRN/mXIxxf4zxoda/T+rUE9RqdcBxM32bU/GUkdaP5dZ/UdJNkj7d+v1cHbNU3+ZcCGGNpH8n6X+2fg7qgGMGABkYg50BxmDt69QxWKeOvyTGYNN1NsdgTETMf6sl7X7Rz3vUIU8ILVHSP4UQHgwh3DbXnXmJ5THG/dKpJ1ZJy+a4Py/1nhDCY62PDc7JRxZfEEJYL+nlku5Vhx23l/RNmuPj1vp42yOSDkn6iqTnJA3FGOutRebsMfrSvsUYXzhmv9c6Zh8IIVTnoGsflPRLkpqtnxerQ44ZABidPAbr5PGX1GFjidNgDNZev6QOOGaMwablrI3BmIiY/8JpftcRM2wtr44xXqNTH1v82RDC9891h84RH5K0Sac+vrVf0vvnqiMhhD5Jn5H0CzHG4bnqx+mcpm9zftxijI0Y49WS1ujUu2WXnm6xfHvV2uhL+hZCeJmkX5F0iaTrJC2S9Mt59imE8CZJh2KMD77416dZtJOe1wBA6uznKsZf0zfnY4kXdOoYrBPHXxJjsHad7TEYExHz3x5Ja1/08xpJ++aoL/9GjHFf6/+HJH1Op54UOsXBEMJKSWr9/9Ac9+d7YowHW09YTUkf1hwdtxBCWaduNH8dY/xs69cdcdxO17dOOW6tvgxJ+med+hvKwRBCqRXN+WP0RX27ufUxyxhjnJT0F8r/mL1a0r8PIezQqY8136RTs/MddcwA4DQ6dgzW4eMvqUPGEqfTKWOJTh2Ddfr4q9UfxmBn5qyOwZiImP/ul7S59e2mFUk/LukLc9wnSVIIoTeE0P/CvyW9QdITvlWuviDp7a1/v13S5+ewL//KCzeZlrdoDo5b62/EPiJpW4zxT14UzflxS/Vtro9bCGFpCGGw9e9uSa/Tqb+f/IakH2ktNlfH7HR9e/JFA5qgU38DmOsxizH+SoxxTYxxvU49f309xvgf1AHHDAAydOQY7BwYf0kdMJZImeuxRKsPHTkG69TxV6sPjMHadLbHYCHGTvmEGM6WcKpEzgclFSV9NMb4e3PcJUlSCGGjTs3CS1JJ0sfnqm8hhE9IulHSEkkHJf2mpL+V9DeS1knaJelHY4y5f2FRom836tTH26KkHZLe9cLfBObYr++T9C1Jj+t//93Yr+rU3wLO6XEzfbtVc3jcQghX6tSX+hR1aiL4b2KMv9N6LHxSpz5297Ckt7Zmv3Nj+vZ1SUt16qN4j0h694u+UClXIYQbJf3nGOObOuGYAUCWThyDddL4q9UfxmDt96sjx2CdOv5q9Y0x2Mz6eKNmeQzGRAQAAAAAAMgNf5oBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcsNEBAAAAAAAyA0TEQAAAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcsNEBAAAAAAAyA0TEQAAAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcsNEBAAAAAAAyA0TEQAAAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcsNEBAAAAAAAyA0TEQAAAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcsNEBAAAAAAAyA0TEQAA0Tlq3AAAEnNJREFUAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByU8pzY5Vyb+yqDp7x8mFiqt0NtLf8ZHvrj81mW8vXNnW1tbwkNWvtzg2FNjfQ5tpje8v39k+0tfzIyfaOUbmn1tby9bH2ronQaGtx9Q6Ot7X8ZLP9h9ymrmNtLb/txLK2li9MtncNhXpbi6vR095F1G5/BhaNtrX80GhvW8tL7fX/isEjba5fenx4SVvLFybaO0blkfYe+JOL21t/cby95dt9GIR2n7fafBy3+zQa23yabrs/kmKbfWp3H9p9bm/nHEyePKb6xGi7ewAAAM4juU5EdFUH9Yor333Gyxef2t3W+uO6Fe116JmdbS3eHBtra/k9f3R5W8tL0tih9l4khUZ7Y73CeHsj6EKbLzqvf822tpa/95uXtrX88qsPtrX8oYeXt7V8ebi943n9LY+3tfyukYVtLS9Jn7r4E20tf92Xfq6t5fuea2+ypnq8vVcwR69t71VY37PtPS299tb72lr+C3df29bysdDe/t735jvaWl6SNnz5HW0t37et2tbyK+5q77nr2be1d00s2Nre8pOL2lpcxfbm+1Q+2d7ysdje8vWe9pYvj7S3vNT+ZE27n29sd0KxPHrmj4MnP/+B9lYOAADOO/xpBgAAAAAAyA0TEQAAAAAAIDdMRAAAAAAAgNwwEQEAAAAAAHLDRAQAAAAAAMgNExEAAAAAACA3TEQAAAAAAIDcMBEBAAAAAAByw0QEAAAAAADIDRMRAAAAAAAgN0xEAAAAAACA3DARAQAAAAAAcpM5ERFC+GgI4VAI4YnTZP85hBBDCEvOTvcAAAAAAMB8ciafiPiYpJtf+ssQwlpJr5e0a5b7BAAAAAAA5qnMiYgY4zclHTtN9AFJvyQpznanAAAAAADA/DSt74gIIfx7SXtjjI/Ocn8AAAAAAMA8Vmq3QQihR9KvSXrDGS5/m6TbJKmrsqDdzQEAAAAAgHlkOp+I2CRpg6RHQwg7JK2R9FAIYcXpFo4x3hFj3BJj3FIu906/pwAAAAAA4JzX9iciYoyPS1r2ws+tyYgtMcYjs9gvAAAAAAAwD51J+c5PSLpb0sUhhD0hhHec/W4BAAAAAID5KPMTETHGWzPy9bPWGwAAAAAAMK9Nq2oGAAAAAADAdDARAQAAAAAAcsNEBAAA/3979x9j2VnWAfz7ZHcqsGAK8iPYrRZMgzQECxkJWoNYwCzQUEwwaQOmKslqAloMCq3+gZqQaFTARIJZobSJtZUUkIag0hQImmBl2q7QsiAVKiytXQwiWBM6lcc/5jaOy+52mXvnvbtnPp9kc+9577n3PE/e7Ozc777nHAAAhhFEAAAAAMMIIgAAAIBhBBEAAADAMIIIAAAAYBhBBAAAADCMIAIAAAAYRhABAAAADCOIAAAAAIYRRAAAAADDCCIAAACAYQQRAAAAwDCCCAAAAGCYhw0iquqqqjpSVXdsGvuDqvpsVX2qqt5fVWdub5kAAADAFJzMioirk+w7auymJM/o7mcm+eckVy64LgAAAGCCHjaI6O6PJ/naUWMf7u4HZ5v/kGTvNtQGAAAATMwirhHxi0n+egGfAwAAAEzcXEFEVf1WkgeTXHuCffZX1VpVra2v3z/P4QAAAIDT3JaDiKq6LMlFSV7Z3X28/br7QHevdvfqysqerR4OAAAAmIDdW3lTVe1L8sYkP9nd/73YkgAAAICpOpnbd16X5BNJnlZVh6vq1Un+JMljktxUVQer6k+3uU4AAABgAh52RUR3X3qM4XdtQy0AAADAxC3irhkAAAAAJ0UQAQAAAAwjiAAAAACGEUQAAAAAwwgiAAAAgGEEEQAAAMAwgggAAABgGEEEAAAAMIwgAgAAABhGEAEAAAAMI4gAAAAAhhFEAAAAAMMIIgAAAIBhBBEAAADAMIIIAAAAYJiHDSKq6qqqOlJVd2wae1xV3VRVn589PnZ7ywQAAACm4GRWRFydZN9RY1ckubm7z01y82wbAAAA4IQeNojo7o8n+dpRwxcnuWb2/JokL19wXQAAAMAEbfUaEU/q7nuTZPb4xMWVBAAAAEzVtl+ssqr2V9VaVa2tr9+/3YcDAAAATmFbDSLuq6onJ8ns8cjxduzuA9292t2rKyt7tng4AAAAYAq2GkTcmOSy2fPLknxgMeUAAAAAU3Yyt++8Lsknkjytqg5X1auT/F6SF1XV55O8aLYNAAAAcEK7H26H7r70OC+9YMG1AAAAABO37RerBAAAAHiIIAIAAAAYRhABAAAADCOIAAAAAIYRRAAAAADDCCIAAACAYQQRAAAAwDCCCAAAAGAYQQQAAAAwjCACAAAAGEYQAQAAAAwjiAAAAACGEUQAAAAAwwgiAAAAgGEEEQAAAMAwcwURVfVrVXVnVd1RVddV1SMWVRgAAAAwPVsOIqrqrCS/mmS1u5+RZFeSSxZVGAAAADA9856asTvJI6tqd5JHJbln/pIAAACAqdpyENHdX0nyh0m+lOTeJP/Z3R9eVGEAAADA9MxzasZjk1yc5ClJvj/Jnqp61TH2219Va1W1tr5+/9YrBQAAAE5785ya8cIkX+zur3b3epL3Jfnxo3fq7gPdvdrdqysre+Y4HAAAAHC6myeI+FKS51bVo6qqkrwgyaHFlAUAAABM0TzXiLglyQ1Jbkvy6dlnHVhQXQAAAMAE7Z7nzd39piRvWlAtAAAAwMTNe/tOAAAAgJMmiAAAAACGEUQAAAAAwwgiAAAAgGEEEQAAAMAwgggAAABgGEEEAAAAMIwgAgAAABhGEAEAAAAMI4gAAAAAhhFEAAAAAMMIIgAAAIBhBBEAAADAMIIIAAAAYBhBBAAAADDMXEFEVZ1ZVTdU1Wer6lBV/diiCgMAAACmZ/ec7//jJH/T3a+oqjOSPGoBNQEAAAATteUgoqq+N8nzkvx8knT3A0keWExZAAAAwBTNc2rGU5N8Ncm7q+r2qnpnVe1ZUF0AAADABM0TROxO8uwk7+juZyW5P8kVR+9UVfuraq2q1tbX75/jcAAAAMDpbp4g4nCSw919y2z7hmwEE/9Pdx/o7tXuXl1ZsWACAAAAdrItBxHd/W9JvlxVT5sNvSDJZxZSFQAAADBJ894141eSXDu7Y8YXkvzC/CUBAAAAUzVXENHdB5OsLqgWAAAAYOLmuUYEAAAAwHdFEAEAAAAMI4gAAAAAhhFEAAAAAMMIIgAAAIBhBBEAAADAMIIIAAAAYBhBBAAAADCMIAIAAAAYRhABAAAADCOIAAAAAIYRRAAAAADDCCIAAACAYQQRAAAAwDCCCAAAAGAYQQQAAAAwzNxBRFXtqqrbq+qDiygIAAAAmK5FrIi4PMmhBXwOAAAAMHFzBRFVtTfJS5O8czHlAAAAAFM274qItyV5Q5JvH2+HqtpfVWtVtba+fv+chwMAAABOZ1sOIqrqoiRHuvvWE+3X3Qe6e7W7V1dW9mz1cAAAAMAEzLMi4oIkL6uqu5Ncn+TCqvrzhVQFAAAATNKWg4juvrK793b3OUkuSfKR7n7VwioDAAAAJmcRd80AAAAAOCm7F/Eh3f2xJB9bxGcBAAAA02VFBAAAADCMIAIAAAAYRhABAAAADCOIAAAAAIYRRAAAAADDCCIAAACAYQQRAAAAwDCCCAAAAGAYQQQAAAAwjCACAAAAGEYQAQAAAAwjiAAAAACGEUQAAAAAwwgiAAAAgGEEEQAAAMAwWw4iqursqvpoVR2qqjur6vJFFgYAAABMz+453vtgktd3921V9Zgkt1bVTd39mQXVBgAAAEzMlldEdPe93X3b7Pk3kxxKctaiCgMAAACmZyHXiKiqc5I8K8kti/g8AAAAYJrmDiKq6tFJ3pvkdd39jWO8vr+q1qpqbX39/nkPBwAAAJzG5goiqmolGyHEtd39vmPt090Hunu1u1dXVvbMczgAAADgNDfPXTMqybuSHOrutyyuJAAAAGCq5lkRcUGSn0tyYVUdnP15yYLqAgAAACZoy7fv7O6/T1ILrAUAAACYuIXcNQMAAADgZAgiAAAAgGEEEQAAAMAwgggAAABgGEEEAAAAMIwgAgAAABhGEAEAAAAMI4gAAAAAhhFEAAAAAMMIIgAAAIBhBBEAAADAMIIIAAAAYBhBBAAAADCMIAIAAAAYRhABAAAADDNXEFFV+6rqc1V1V1VdsaiiAAAAgGnachBRVbuSvD3Ji5Ocl+TSqjpvUYUBAAAA0zPPiojnJLmru7/Q3Q8kuT7JxYspCwAAAJiieYKIs5J8edP24dkYAAAAwDHNE0TUMcb6O3aq2l9Va1W1tr5+/xyHAwAAAE538wQRh5OcvWl7b5J7jt6puw9092p3r66s7JnjcAAAAMDpbp4g4pNJzq2qp1TVGUkuSXLjYsoCAAAApmj3Vt/Y3Q9W1WuT/G2SXUmu6u47F1YZAAAAMDlbDiKSpLs/lORDC6oFAAAAmLh5Ts0AAAAA+K5U93fc6GL7Dlb11ST/eoyXHp/k34cVsnw7rd9k5/W80/pNdl7P+p2+ndbzovr9we5+wgI+BwCYqKFBxHGLqFrr7tVl1zHKTus32Xk977R+k53Xs36nb6f1vNP6BQCWx6kZAAAAwDCCCAAAAGCYUyWIOLDsAgbbaf0mO6/nndZvsvN61u/07bSed1q/AMCSnBLXiAAAAAB2hlNlRQQAAACwAyw1iKiqfVX1uaq6q6quWGYto1TV3VX16ao6WFVry65nO1TVVVV1pKru2DT2uKq6qao+P3t87DJrXKTj9PvbVfWV2TwfrKqXLLPGRaqqs6vqo1V1qKrurKrLZ+OTnOMT9DvlOX5EVf1jVf3TrOffmY0/papumc3xX1bVGcuudRFO0O/VVfXFTXN8/rJrXaSq2lVVt1fVB2fbk5xfAODUs7Qgoqp2JXl7khcnOS/JpVV13rLqGeynuvv8Cd8m7eok+44auyLJzd19bpKbZ9tTcXW+s98keetsns/v7g8Nrmk7PZjk9d399CTPTfKa2d/dqc7x8fpNpjvH30pyYXf/SJLzk+yrqucm+f1s9Hxukv9I8uol1rhIx+s3SX5j0xwfXF6J2+LyJIc2bU91fgGAU8wyV0Q8J8ld3f2F7n4gyfVJLl5iPSxId388ydeOGr44yTWz59ckefnQorbRcfqdrO6+t7tvmz3/Zja+yJyVic7xCfqdrN7wX7PNldmfTnJhkhtm41Oa4+P1O1lVtTfJS5O8c7Zdmej8AgCnnmUGEWcl+fKm7cOZ+C/3M53kw1V1a1XtX3YxAz2pu+9NNr7YJXnikusZ4bVV9anZqRuTOE3haFV1TpJnJbklO2COj+o3mfAcz5btH0xyJMlNSf4lyde7+8HZLpP6mX10v9390By/eTbHb62q71liiYv2tiRvSPLt2fb3ZcLzCwCcWpYZRNQxxib9P1AzF3T3s7NxSsprqup5yy6IbfGOJD+UjWXe9yb5o+WWs3hV9egk703yuu7+xrLr2W7H6HfSc9zd/9Pd5yfZm40VbE8/1m5jq9o+R/dbVc9IcmWSH07yo0kel+SNSyxxYarqoiRHuvvWzcPH2HUy8wsAnFqWGUQcTnL2pu29Se5ZUi3DdPc9s8cjSd6fjV/wd4L7qurJSTJ7PLLkerZVd983+2Lz7SR/lonNc1WtZONL+bXd/b7Z8GTn+Fj9Tn2OH9LdX0/ysWxcH+PMqto9e2mSP7M39btvdlpOd/e3krw705njC5K8rKruzsZpkRdmY4XE5OcXADg1LDOI+GSSc2dX6T4jySVJblxiPduuqvZU1WMeep7kp5PcceJ3TcaNSS6bPb8syQeWWMu2e+gL+czPZELzPDuX/F1JDnX3Wza9NMk5Pl6/E5/jJ1TVmbPnj0zywmxcG+OjSV4x221Kc3ysfj+7KVirbFwvYRJz3N1Xdvfe7j4nG//2fqS7X5mJzi8AcOqp7uWtvJzd7u5tSXYluaq737y0YgaoqqdmYxVEkuxO8hdT7Lmqrkvy/CSPT3Jfkjcl+ask70nyA0m+lORnu3sSF3g8Tr/Pz8aS/U5yd5Jfeuj6Cae7qvqJJH+X5NP5v/PLfzMb102Y3ByfoN9LM905fmY2Lla4KxuB9Xu6+3dnP8Ouz8ZpCrcnedVstcBp7QT9fiTJE7Jx2sLBJL+86aKWk1BVz0/y69190VTnFwA49Sw1iAAAAAB2lmWemgEAAADsMIIIAAAAYBhBBAAAADCMIAIAAAAYRhABAAAADCOIAAAAAIYRRAAAAADDCCIAAACAYf4X0oV1coL9gTMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.11094913 0.09698259 0.10752105 0.09474361 0.09148202 0.16456628\n",
+      " 0.15387052 0.152372   0.14834218 0.14599247]\n",
+      "[0.1032799  0.08838192 0.12270175 0.09715664 0.0845587  0.326461\n",
+      " 0.297051   0.31178892 0.2632538  0.22024405]\n",
+      "-6.269343376159668\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.09684234 0.0930611  0.0921976  0.09144492 0.09054102 0.08960709\n",
+      " 0.08869405 0.08780377 0.08692678 0.08821139]\n",
+      "[0.08726858 0.08421258 0.08407459 0.08351622 0.08251345 0.08137595\n",
+      " 0.08021979 0.07905813 0.07787655 0.07845528]\n",
+      "-8.471969604492188\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uWttaVddU1S2z25OObJsAAAAAPFGH8omfTyTZccDaZUmu7e6zk1w72wYAAABgA1kz+Onu65LsPWD5wiRXzO5fkeQN69wXAAAAAAs63Gv8nNbddyXJ7PbUqQdW1SVVtauqdt23d/9hHg4AAACAJ+qIX9y5uy/v7u3dvX3L1k1H+nAAAAAAzBxu8HN3VZ2eJLPbPevXEgAAAADr4XCDn6uTXDy7f3GSz65POwAAAACsl0P5OvdPJfn3JM+vqt1V9fYkf5zk1VV1S5JXz7YBAAAA2EA2r/WA7r5oovSqde4FAAAAgHV0xC/uDAAAAMByCH4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABjU5kWeXFW3J7k/yf4kj3T39vVoCgAAAIDFLRT8zLyiu+9dh/0AAAAAsI6c6gUAAAAwqEWDn07y+aq6oaouOdgDquqSqtpVVbvu27t/wcMBAAAAcKgWPdXrZd19Z1WdmuSaqvpmd1+3+gHdfXmSy5PkZ37uKb3g8QAAAAA4RAt94qe775zd7klyVZLz1qMpAAAAABZ32MFPVZ1YVU9/7H6S1yS5cb0aAwAAAGAxi5zqdVqSq6rqsf38dXd/bl26AgAAAGBhhx38dPdtSV60jr0AAAAAsI58nTsAAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMaqHgp6p2VNXNVXVrVV22Xk0BAAAAsLjDDn6qalOSDyd5bZJzklxUVeesV2MAAAAALGaRT/ycl+TW7r6tu/cl+XSSC9enLQAAAAAWtUjwc0aSO1Zt756t/ZiquqSqdlXVrvv27l/gcAAAAAA8EYsEP3WQtX7cQvfl3b29u7dv2bppgcMBAAAA8EQsEvzsTnLWqu0zk9y5WDsAAAAArJdFgp8vJzm7qp5TVccneXOSq9enLQAAAAAWVd2POzvr0J9c9bokH0qyKcnO7n7fGo+/J8m3Vy2dnOTew25gfOazNjOaz3zmM5+1mdF85vN4P93dpyy7CQAAViwU/Cx88Kpd3b19aQ1scOazNjOaz3zmM5+1mdF85gMAwEa3yKleAAAAAGxggh8AAACAQS07+Ll8ycff6MxnbWY0n/nMZz5rM6P5zAcAgA1tqdf4AQAAAODIWfYnfgAAAAA4QgQ/AAAAAINaSvBTVTuq6uaqurWqLltGDxtNVe2sqj1VdeOqta1VdU1V3TK7PWmZPS5TVZ1VVV+oqpuq6r+q6p2zdTNKUlVPqaovVdV/zubz3tn6c6rq+tl8/qaqjl92r8tWVZuq6itV9Y+zbTOaqarbq+rrVfXVqto1W/MeW6WqnllVn6mqb85+H/2CGQEAsJEd9eCnqjYl+XCS1yY5J8lFVXXO0e5jA/pEkh0HrF2W5NruPjvJtbPtY9UjSX63u1+Q5KVJLp29bsxoxUNJXtndL0pybpIdVfXSJH+S5IOz+fxfkrcvsceN4p1Jblq1bUY/7hXdfW53b59te4/9uD9L8rnu/tkkL8rKa8mMAADYsJbxiZ/zktza3bd1974kn05y4RL62FC6+7okew9YvjDJFbP7VyR5w1FtagPp7ru6+z9m9+/Pyv9snREzSpL0iu/PNo+b/ddJXpnkM7P1Y3Y+j6mqM5O8PsnHZtsVM1qL99hMVT0jycuTfDxJuntfd383ZgQAwAa2jODnjCR3rNrePVvj8U7r7ruSleAjyalL7mdDqKpnJ3lxkutjRj80O4Xpq0n2JLkmybeSfLe7H5k9xHst+VCS30/y6Gx7W8xotU7y+aq6oaouma15j/3Ic5Pck+QvZ6cLfqyqTowZAQCwgS0j+KmDrPlOeQ5JVT0tyd8meVd3f2/Z/Wwk3b2/u89NcmZWPln3goM97Oh2tXFU1QVJ9nT3DauXD/LQY3ZGSV7W3S/Jyqm4l1bVy5fd0AazOclLknyku1+c5IE4rQsAgA1uGcHP7iRnrdo+M8mdS+jjJ8HdVXV6ksxu9yy5n6WqquOyEvp8srv/brZsRgeYnXryxaxcC+mZVbV5VjrW32svS/KrVXV7Vk4xfWVWPgFkRjPdfefsdk+Sq7ISIHqP/cjuJLu7+/rZ9meyEgSZEQAAG9Yygp8vJzl79k06xyd5c5Krl9DHT4Krk1w8u39xks8usZelml2L5eNJburuD6wqmVGSqjqlqp45u39Ckl/JynWQvpDk12YPO2bnkyTd/QfdfWZ3Pzsrv3f+ubvfEjNKklTViVX19MfuJ3lNkhvjPfZD3f0/Se6oqufPll6V5BsxIwAANrDqPvpnNVTV67Lyl/ZNSXZ29/uOehMbTFV9Ksn5SU5OcneS9yT5+yRXJnlWku8keVN3H3gB6GNCVf1ikn9N8vX86Pos787KdX6O+RlV1c9n5aKym7IS6F7Z3X9UVc/Nyqdbtib5SpK3dvdDy+t0Y6iq85P8XndfYEYrZnO4ara5Oclfd/f7qmpbvMd+qKrOzcrFwY9PcluS38jsPRczAgBgA1pK8AMAAADAkbeMU70AAAAAOAoEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAM6v8BPDUWt7FLLwsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.25494194 0.19181566 0.22277483 0.26271814 0.25406656 0.26001623\n",
+      " 0.15522124 0.24388857 0.26437804 0.19996487]\n",
+      "[0.40944967 0.35421196 0.36972898 0.42281675 0.3704151  0.4056993\n",
+      " 0.15887561 0.36994043 0.41490847 0.32014498]\n",
+      "-7.524182319641113\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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cMfGyid+Hzz43+6p0/YP1DNXxbyl4xw8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT010PaCUcigifioiroqIcUTcUWt9QyllV0T8XERcFxH3RcTX1lqPrWcyw2ff0MzOXDmXjp08PVrPqVPHb8iXaTx1U5pv+bW7mtnoJc9Px04cP5Pmp27c0Z7XZMmPfWac5lvufrSZje79VMexa5pPHj2V5if/1xc3s/kr837lznsX03z6dHtNT7/k+nTs8es7P2WaZh7P13vH/UtpPjq2rk8vAJ5hNrQGKxExHDbj8VT7tXVxW3tcRMSZvXm9EdPTzahOdBx7z2Saz+/Lx2/5aPu6Ru1pRUTE4ra1f390/uq8Dpp5LF+z8baZZjaYytdkNJvXKvN78+uqydRKFkZEqe15R0TseGC2Pa99U+nYxW1ddW17zRd25de83J5WRETsnms/oEx01IajvP6L2pFfUO3PnzrO7+ELalPX5BmqdPw7XDfx+eSScD6vaMsR8fpa63Mj4iUR8c2llOdFxHdGxDtqrc+OiHes/B0AgI2hBgMA1q2z8VNrfbjW+v6VP5+MiLsj4kBEfEVEvGXlYW+JiK+8UJMEALjcqMEAgI2wqvewllKui4gXRcR7ImJfrfXhiHOFSUTsbYy5vZRyZynlzqU4u77ZAgBchtZbgy2OFy7WVAGAS8x5N35KKVsj4hci4ttqrSfOd1yt9Y5a66211lsno+OHqQEA+Gs2ogabGuS/ewUA6K/zavyUUibjXMHx07XWX1z58JFSyv6VfH9EHL0wUwQAuDypwQCA9eps/JRSSkS8MSLurrX+8FOiX42I1638+XUR8SsbPz0AgMuTGgwA2Ajnszf150fE10XEB0spf7byse+OiO+PiLeVUr4hIj4dEV+z3smcuWFXM5t55HQ69sRN29N89tDBZrb8wOF8Xvvy7e/m9+f9s+t/sb29+OTHHkzHlmT7yIiI7e9vr8vyAw+lY+/797el+d7Jp/2VARERMdexnfuOu59M8/nrd6b5iWvb20se+O3H07G1azvD5eVm1LVd+7YHRvm5k11lhwv5fdQ5bwAuNxtYg5WIZNvp+b35FuGZpS3561uZSbZzn8xfd0/vz7drz153u0yeyuc9XMrzwdl2Xgd5bdi1lfx4ur0ug7L27dgjIha35w8YtMukGHd85TD7WJ7X6fZ9NprM5zV/VZ7PHmlnXfMe5zvJp6ptsjdexz1uu/c1uFS/zvD5c9F0Nn5qrf8zIlp3yhdv7HQAAIhQgwEAG2NVu3oBAAAA8Myh8QMAAADQUxo/AAAAAD2l8QMAAADQUxo/AAAAAD2l8QMAAADQU53buV9MddDasTTi9HVb07GPfPnZNN/xwdlmNrH/qnTss37s02l+9/+9L80H27Y1s0/8yxvTsTf+9ONpXu873MyWX35LOnY8ncYxWKr5A7Jjz0ym+eL2YX7u5Okcz+bHXtjXfq4jIubG42a2/51PpGPrTP4p88Tz28/1zPIoHbvldz6c5u1ZA0CHUqJMtF/DTl/d/l7g1PG8HigdL1B1qv263fWafubK/NzTx9q1Y5eJhTxf2JHXKlOn2he+vD1/zR8u5PXEeDo59zD/vm3n85FfViwnUxvnT1eMJ/Lno861i88zV+Zjz+7KL2yw1F6XUUfNuzyb32d1sr0opXRcc3TU06Xj+/B1cyrAknxdFhFRx2v/OmHdutYss0nrSUPH58+mqpt4j18A3vEDAAAA0FMaPwAAAAA9pfEDAAAA0FMaPwAAAAA9pfEDAAAA0FMaPwAAAAA9pfEDAAAA0FMTmz2Bp5r75LFmdvyFe9Kx47PDNB/d/fFmNrjleenYsjRK82f/l+V8/P69zezKu8bp2IWD29N8dqF97E9+Tb4mw9M1zWd+7b1pnil//rE037FwfZqffekVzWx45Ml07Nzps2me3Qv1pS9Mxy7tmErz5bl2dmoufz7mX/tZab77J96V5gDQViNG7Xqm5i9RqeFiSfOyuNTMlrfvTMd2zev0wbyOqtPt1+0ze/J5D/NyIkYz2fdP89pxaUc+7/l97XlPf3QyHTsx317viIjJE9NpntUytetbxvmSxngueT725nVp2Zs/IYtnZprZaKbj2PnTEXUy+ZJp0LEoNT93jDvyso7v09eOC1uHMsif7Np1XZulaz0v4JrxDFM6/kG7FCWfdt7xAwAAANBTGj8AAAAAPaXxAwAAANBTGj8AAAAAPaXxAwAAANBTGj8AAAAAPXVJbee+tHdbM9t55yPp2O1vP5rm2cZ8Z/cm+1ZGxCO35Vt4zz6ab1d41a8caWY77sy33DxzU3u79oiIk3+rnU9esZCO3fMH+XWvx2B7vg197dgeb99bP9zMlk+cSMeWiY7bOjn3aC4fuzyX90rP7Euuq2NXy6njeQ4A65K8/i1taQ/b8lD+Ara4o+MFbtjek/3MlXmNNTHfsVV8x6nrZPvcE2fysaVjO+rZx9vVZZlbTsfuvuZkmi9+aE87TLaoj4goS/lW8hNn8uuqE+01n8hLy5g4k2+FndV/g3zaMbMlP/mJve3nejibH3y8lNd3yzvbW8VPXMpbPm/i1uXZdu+X7FbvEbZ7p7e84wcAAACgpzR+AAAAAHpK4wcAAACgpzR+AAAAAHpK4wcAAACgpzR+AAAAAHpK4wcAAACgpya6HlBKeVNEvCYijtZaX7DysX8XEf8sIh5dedh311p/o/NYU5MxcdXBZv7xL5tpZvv/ZG967K1Ly2k+np9vZicOTaZjr337k2l+zz/dluZX/Xb7uu75lv3p2Btf/+40n3jpC5vZs773bDr2sVu3pPn4C1/UzAZ/cFc6dvTYY2k+PJvPbXTiRDMrk1Pp2Lq0mOYT1x5qZosTJR1bRjXNd774SDN75MEr0rHjyfw+nDh4oJktH34wHQvAM89G1mAxGETdMts+1ziZxyg/9MyjHd9HHLcPvnBF/rp75uq8viujrtft9uQn5vPX9MgPHROn29dVzw7TsY9/bHea7zuZzK2j5q3D/PkYTadxDBbb555sl9MRkd9HERGD5fYDJk7nCz4a59c1Mddel7272nVlRMTRJ7an+cLe9qJtG+bPdST3YERE7fo2/LjjPl2Pkpy8djyZwDPK+bzj580R8aqn+fiP1FpvWfmvu+AAAGA13hxqMABgnTobP7XWP4yIJy7CXAAAWKEGAwA2wnp+x8+3lFI+UEp5Uykl/xkWAAA2ihoMADhva238/FhE3BgRt0TEwxHxQ60HllJuL6XcWUq5c3F0Zo2nAwAg1GAAwCqtqfFTaz1Sax3VWscR8V8j4rbksXfUWm+ttd46NWz/UkEAAHJqMABgtdbU+CmlPHUrqq+KiA9tzHQAAGhRgwEAq3U+27m/NSJeHhF7SimHI+J7IuLlpZRbIqJGxH0R8c8v4BwBAC47ajAAYCN0Nn5qra99mg+/cS0nq4tLsfzA4Wb+rJ+camajj38yPfYTr31Jmm9/a/u848l0aDz4xTvT/KY3n8gPMB43oxtf/+506PD5N6f56F1/3swefftN6djF3y1pPnjLXWmemThwdZrX7VvS/NTzn9vMtv9G/s3NurSY5uMjjzaz+/7tvnRsOZ2/SW7b2fY9PLPjbH7s+9tjIyIe+oprm9neH30wHQvAM89G1mAxGESdm27G46naHjrqOHZ76Dln26/Li9vyWmRy10KaDz+6Nc2Xt8+keWawnOdl3L7wsjBMx45n27VhRMTybDK+5Gs22pIXtqXj+RokZdRyx3LW/LLTmnhiPh964kheO05e0b5Xtk7mteGJ2Txf3NLTH5Ws+X14WbIm9NR6dvUCAAAA4BKm8QMAAADQUxo/AAAAAD2l8QMAAADQUxo/AAAAAD2l8QMAAADQU53buW+k0a4tcfzV7W3Xtzyy1MzKgc9Oj739rfm26JnSsWvfro+05xURcey529J896ceamaP3f7SdOyeO96V5sN9e5vZlf/gkXTs6MTH0vzsqz+3mU3/xvvSsYvXt+cVETE4m++RuvW/v6eZrXeTxXL9oWb23B8+kY695/Zdab7w4Z3NbOJ0vv3qwt78yg5979rvcQAuc7VGSV57Zx5tv0ZNnsxfs8u44/uIg3betWX6zExegy1M53uTl1HXXvNtg6V87HCx/bo9PJNvqT44np97NJWdOF/vOsjrjamT+XWd2dM+fhmlQ2N4pqNKS7aiH03nQ2OQz3t6un0zDQf5vMbjfM3KODl3x3oDXAq84wcAAACgpzR+AAAAAHpK4wcAAACgpzR+AAAAAHpK4wcAAACgpzR+AAAAAHpK4wcAAACgpyYu5skGoxrTJ8bN/PRVk81s111PpMee/7LPTfPp33xfM9v3zkfSsQ+/cn+a7/+5j6Z53b+3me25413p2NEXfXaaDz94fzO7703XpGPnfmtbmu/+iXxumcEf3bXmsV3OfOVtaT77y+9N8/HMVDMbPvJ4OvbGn5tN88e+a6GZHXtwRzr25v/9/Wk+eNb1zWx076fSsQBc3urkMBav3t7Ok28FDkY1PXZpl3bnjEbtse0oIiJOPjmX5lOLpePkiY6hy7P5A+qgnS/vyC9seHKY5oOlJBznz8dwIT93GbXr7Yj8Xpg60XEvdNwro9n2uUfT6dDYd+hY/oDEgbnjaX74eF6jZfdKHXV9AnToeD4BNoJ3/AAAAAD0lMYPAAAAQE9p/AAAAAD0lMYPAAAAQE9p/AAAAAD0lMYPAAAAQE9p/AAAAAD01MRFPVuNGJ4dN+MrfvnDzezEl70gPfSWn3/Pmqd19ppdab7908tpPnr8iTQflrLqOf3l2N97f37u5NjXfc+edGyZfyjN6003ts/7sU+kY8dfcEuaL22bTPPp33xfM5v95femY7uc3TvbzB7+rivTsdu3zKf5ibt3N7PBMJ/Xg//9pvzcP7OtmW2991P5wQG4rI0nSpzZ037tHc20x544lL9mL891nHx6qn3e6Y6xJ/JSdeJMPrzUmoxtZxERw8WO/Ey7Ppx5KL+whQNLaZ7VSXU6fz6WtuZrtrg9r0vH2fB8STrVYfvcE3mJFUce2Znmz72+XdeeXM6fjy85dE+a/+6VL2lmuyby9a6jUZpHco9GRMR4nYueKcl7AGr7a7b1nza/B+uFvOYu2ZpEXNB1gQvJO34AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnOrdzL6W8KSJeExFHa60vWPnYroj4uYi4LiLui4ivrbUe6zxWjRgstbfAe/xrPquZXfn7D6bHzjdcz40n8y0FH3lpvg93/YcvTPPrX/vn7WN/6+elY/e973SaZ9t9zr7//nTsqRdfn+Yzb1/7tulllG/DOPeJJ9J84ZW3NrOubWV3v/Fd+bk/9mgzu+7rHknHLn7h30rzL//B329mv/CpfIv7J4+0t2uPiDj4Wx9pZjaWBOifjazBOs+V7DhdOnZWnnm84+CL7a3Lp4/nBz+1Nd8Ke/5gfuo6bH+Ps6v+mzqZv7qOZttl9PLWjq3gT+a15dYH29ddlvKqdzSTf1931HHd46l2dvrqfOxwMa/RJhba67J4RceaTef3wnO35zVc5p2Hb8rPncz7gsu2Pt+srd4jLt9tzbN1uVzXhGeE83nHz5sj4lV/42PfGRHvqLU+OyLesfJ3AAA2zptDDQYArFNn46fW+ocR8TffnvEVEfGWlT+/JSK+coPnBQBwWVODAQAbYa2/42dfrfXhiIiV/+/duCkBANCgBgMAVqXzd/ysVynl9oi4PSJienrnhT4dAADx12uwqTk1GABcrtb6jp8jpZT9EREr/z/aemCt9Y5a66211lunpras8XQAAMQaa7DJ6a0XbYIAwKVlrY2fX42I1638+XUR8SsbMx0AABJqMABgVTobP6WUt0bEuyLi5lLK4VLKN0TE90fEl5RSPh4RX7LydwAANogaDADYCJ2/46fW+tpG9MWrPdlossTpq6aa+Z5f/kh77PUH02Pf+60vSfNn/at3N7OZh0+lYw++M/8RtYn/8wN5fu2hZnbVG/4kHVs/74VpPndP8x3e8cRPbU/H/qNrfzPN3zr96qm+QHwAACAASURBVGa25Rfek44dzi+meZSSxpO/fWcz250fOZa/+HPSfH5H+7bf/eaFdOy/PXRHmn/jf/mWZvafvvGN6djvv+Mfp/n49HyaA9AvG1mD1UHE8mzy/b7kZXlxW/6avbSt4+QT7dfd8TAfOjiRl6rjmXGaL29pjx8s1XTsaDq/7uFiO9/+3MfTsbvmzqT5I0fbtePWT+e/r+ns9nxRz3YUUnMPt9flzJX5mizP5vnUyfbzVW/I65yvfc5daT4o7Xm/cvsH07HvPHxTmk9kT1fN78EupaMmrjW5Twf52E7j/HMgVTreP7COdSnrvK66nutaj641WY913mdwAe9OAAAAADaTxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPTUxMU82fDsOLZ/ar79gL17mtHRF29Pj/2sf/WutU4rTt+QH3vrRx5P83L1/vwE47raKf3Vsf/kz/MHXHdNM5q4o72eERE/uec1aT4x25736a9+cTp2sJxf85HPGaZ5uXm2mV33n9KhMfGOP03zmWsPNbOT/+FgOvb1V/2LNP/qb//9ZvbNv/b16dg9u0qan/r625rZ7jeu/f4H4DKRvDTX5FuBizs6jpu/fEVMtsvNsx2vfZMn8kNPPNJVyi42k8EoH1k6yrc6aM/92BNb07FTE/nJF3e0T760bSode/rqfE3PXNtek4iIhSvbNdrkifx7xsOzHYuWTG3pzGQ69H8cfk6af+uz39nMXv+Rr03H3rb//jT/o0O7mtmeyfz5iNE4zzuU8drH19rxfCT3cKeur2/KOt5fUNe5Zuu5rg51HV/Xrct61hPCO34AAAAAekvjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAemriYp5sadsgHvzCrc389HMmm9l1P7t0IaYUERFb3nF3mt/7XS9I8z0fqGm+/efvbGb3fd9L07HX/Zt3pfl4x5Zmdnpf3tfbcnSc5nO/+J40zyy85rY0v/FnHkvz0d0fb2YP/tLz8rGj/Pna8uvbmtnClx9Px9a6kOYfPrm/mc09lD8fh27/WJqfeV37c2c5HQnA5W48ETF/VWnmC88708xmPjybHnsqf+mMOmy//g0W87G7v/CRND/8yBVpfuqR6WZ2/Mb83NNPdn1/dNhMnnPNg+nIw8d3pPncw+3navJYXovMHZlK86Vt7Xo7ImJxb7uqWL4if8Ien5hJ88mT7S899uzNa8MtU/m5f/XRW5rZoe3H0rH/2652rR4R8Y6dL2xmZTpf71jK551/FRERpX0vRM1HJyPPGedfC2TqhXz7wHgT35tQ8zUpg85VfUaq4847kWc47/gBAAAA6CmNHwAAAICe0vgBAAAA6CmNHwAAAICe0vgBAAAA6CmNHwAAAICeuqjbuQ/PRuy8d9TMD/zAnzazxVd9bnrsY7/+7DTf/oPtLbyXJvP+18RNJ9N84QX5VvNzRz6rme3+YL513qPfmG/3vrCnvaXgb93+H9OxX/Xn/yTNF7e0z73zv+XbzC/P5lsdnn5Wvv3qlvGzmtmh78y3xRzdc2+aP/5P29d1zf9xOj/27vZ9FBFx8kPt8X/vve9Mx/7ud/wvaT534pNpDgAt48mIhSuTmuPR9rbn89e0t/eOiDg7n9dRu+5pv+Yvb02Hxo078i2+pyfyuT20/1AzWzp0Nj/5TXl9t/zgXDN75d6PpGNfcu0n0vy1j35jM9v6YL5oZ5LaMCJidMN8mpfl9vM5eLBju/ZT+bmXt7XvwcXlYTr22PG8dvyyz/pwM3vtjrvSsd/5wN9N8xivfUv1KPnnRyn5+DpYx/fpu7ZrHyZrvolbxccwP3rtWvN1ye/D6Om256XjslN1Hc81Gyt5KrzjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnJtYzuJRyX0ScjIhRRCzXWm/NHr88F3H0c9q9pqW/0x4+9VA+1Ru/bZTmZ68pzWywmGx4HxEL81NpvvXt29J8+uOfbmbLc1enY3d89HSaR63N6DXz354OnTrRHhsRcfK69pot3/7SdOzylvbYiIj5q/Jzf+8PvqOZfdcffHU69ppfuy3Njz2/fe7BUv58XPGWd6X5zj/e1cze/h9vScfu+M13p3mdm0tzAC4fq63BytQ4yqH5Zn5g14lmdsvuw+lc3v/YoTR//P6rmtnCVcvp2M/e1q6hIiKWxsM0fyQpD+uZfOzifJ7PHG/XtHef3p+Off+Ja9J89oozzezx529Px545kK/p1DCve7/9Rb/dzH7mgbzGeuDD7ec6ImK8vT2351xxLB37kY41vXnm4Wb27x96VTr23R+/Ic2nTyV17aDj++gTHV9uDfKauYzzmjlV8+c61nHsmnwNEhERJb+u9Shd516PcceaDS/cda1H5/NxQeX/VnIRLbfvz3U1flZ8Ua31sQ04DgAA508NBgB08qNeAAAAAD213sZPjYjfLqX8aSnl9o2YEAAAndRgAMB5We+Pen1+rfWhUsreiPidUspHa61/+NQHrBQjt0dETOy8Yp2nAwAgVluD7dmxGXMEAC4B63rHT631oZX/H42IX4qIz/iNb7XWO2qtt9Zabx1u2bKe0wEAEGuowbarwQDgcrXmxk8pZUspZdtf/DkiXhkRH9qoiQEA8JnUYADAaqznR732RcQvlXNb9U1ExM/UWn9rQ2YFAECLGgwAOG9rbvzUWj8ZES9czZgyM4qp55xo5oMPtn/+fHmu5vN55NE0nxmN22OH+RuftnzoqjRfmkvjOPWiA83soS8YpmMP/GH+1uwHvrg9/uYfP5qOLafPpHndsbWZjT58Tzr26L/4vDS/4Tveleb/7T9/fjO76cDZdOwDf7s974iI8cxyMxv8vSfSsZ/80lvS/IbXt++zHe9+dzr22Otemuanry7N7OD/8yfpWAD6Yy012NTEcly7t/0aNzux1MwGJa/BHrr3yjTfng3PDx0vnL0/za+caNeVERF/fPOzmtm3vuR307E/9qGXpfm1z3m8mW2fWEjHfuiJ/Wl+5sRMM9v1aL5oC3vb9UJExOiBvHD9wfGXtMOOe2H60Kk037u9nZ9ZnkzHfuGNH0/z7//4q5rZVVtOpmP3X3UszR86u7uZ1bn2cxURkT8bEbHcrkvPnaC95jXJIiJi3JHXdt2anTfiPK4rO3d23vPRdV3r0LmmF9J47evS+XxwWSiL7TvBdu4AAAAAPaXxAwAAANBTGj8AAAAAPaXxAwAAANBTGj8AAAAAPaXxAwAAANBTa97OfS3Gy4M4/Wh7C8lnvbO9vfgnv3I6PfYnf+L6NJ9+b3uL76t//3g69o3f9IY0v2FiMc2/9Hv/dTN77z/4oXTsv33FF6X5LYNRM7vuSx9Lx/74216d5iXZXfLgjnwX2YW9aRzlRc9P8/rph5vZ4MP583X9gzvS/JFXH2pmO77p3nTs7oMH0vz4iw82s5fdlW/R+M6HH03zQ/+m/TmwiRtPAvAMMDkYx77Z9pbWN25pvwbtmci36N7/rPz166GJPe157Tibjr1yMJ/mL9uWxvHuW/6smX3plo+kY6+6Ja83dg3b63Ld5JPp2Csm8uv66I59zezdR/Ma6tbPzmuZB0/lddJo3P6+8JOnZtOxXc6Ohs3s8KNXpGM/PZHng0G7GvrSA3enY6/YczrNf+z4y5rZ8p721xgRERPHOr7PvpRv515G7fqxdG3/vZ7t3ruOvY5zr3vL9PVs596xlXzntugXcrv3C7hN/abqWHM2ULGdOwAAAMBlR+MHAAAAoKc0fgAAAAB6SuMHAAAAoKc0fgAAAAB6SuMHAAAAoKc0fgAAAAB6auJinuzQtifiB1/x0838y//ufDN79T2vTo995K3Xpvnxm8fNbP7QlnTsd3zjN6X5Yy+cSvNrfuP+Zva193xzOvbRz5pN8wO/driZ3fN4PnbrV9c0P3tFaWaf+Jr82OPZ5TQ/cfO2NH/sO4bN7Cdve3M6dqbk537dXV/fzA5+/dZ07PO33p3m8+NPNLNf//6Xp2N3/My703x48EAzy68YgMvd9GApnr3laDP/qh3vb2YPLO9Mj33ttmNp/gUv/mQzOzvOS9H3nz2U5j/95NVpfmY02czuX74iHft7x5+T5luHZ5vZUm3XMRERH3ii/ZoeETEctOvWF3zevenYz9nx6TTfO70rzQ/Pt5/v77v5l9KxHz2bPx/DaF/Xw1fn99mp5ek0f/bskWb2y4/cko7dOtl+LiMixqPke+XjvJ6uk/m9UEq73o6IqDU5/qi9niuD83zcHl86risb23XuzmN3zbvLesZ3Xdd6rPe61nXqzTs3F1Hy74l3/AAAAAD0lMYPAAAAQE9p/AAAAAD0lMYPAAAAQE9p/AAAAAD0lMYPAAAAQE9p/AAAAAD0VKm1XrSTbdt+sN5627c086nHTjezoy+5Ij32Vb/6yTQfP3m8mdXn35iOXdo5k+bT7/t4mh//0uc2s633z6djhycX0vz0s9rrMvNr703HrkeZnk7z+7/rc9L8hjcfTvPl+z696jn9hYkDV6f5vd90bTO74W3H0rHjD3x0TXPaCItfemszm/ofd17EmQC0vae+I07UJ8pmz4O/bvvN++pLfvy1zfzsaKKZ3bTjaHrs3/7zF6T55OPtYy9vG6dj68wozQcn28eOiBjvWGqHo47bdCn//mhJxk8+mY+dPJWfezxsZ8PFdGgcevV9aX7Pg/vSfOYjs81sYU/H8zWZf11x8Kb2vbR16mw69oEnd6b5qce2NLPZT0+mY6faXyacO/ah9nU962dOpGMHZ5N7MCJilK9pZF+rdXwdVy7gsTvz8TqO3WU948cda3IhXcSvu7k8/cljb4vji0ef9gXGO34AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCn8j0wO5RSXhURb4iIYUT8RK31+9MBg4jRbLvXlG2VvfB3Pi89dN2xLT/19FT7vMtdWx12xIv5vprb7mtv2V7/9CP5wbdvTeNTL9vTzPJN6Nenns233Bx0bDW6eGh3mk883t5WfXzyZDp2+cGH0nw82d7OfTzXvk8iurexL6W9Pet4YSEd28WW7QD8hdXWYIsnp+L+P2i//mXbWf/+7qvTuez9eBrH9PH2lux1kG9rvrAz34a7dJRwi9vbr9vTx/MCL9tSvcvkfH7sQUftmV3X5Ol8i/v7pq5L8yuO5HObPtE++eCjHde1lOfHPrW/mZ1YTofGtiP5mu1Ybp97MMoP3jXvuSPtm6GM8uejU8fnQGSXndSdERG1I88Hb+KW6+tUsq3koc8G7V7Lmt/xU0oZRsSPRsSXRcTzIuK1pZTnrfV4AAB0U4MBAKuxnh/1ui0i7q21frLWuhgRPxsRX7Ex0wIAoEENBgCct/U0fg5ExANP+fvhlY8BAHDhqMEAgPO2nsbP0/3Q6Gf8QGUp5fZSyp2llDuXFk+v43QAAMQaarDRvBoMAC5X62n8HI6IQ0/5+8GI+IzfqltrvaPWemut9dbJqS3rOB0AALGGGmw4pwYDgMvVeho/74uIZ5dSri+lTEXE34+IX92YaQEA0KAGAwDO25q3c6+1LpdSviUi/kec20r0TbXWD2/YzAAA+AxqMABgNUqtn/Ej4RfuZKU8GhH3P+VDeyLisYs2gX6wZqtnzVbHeq2eNVs9a7Z6z5Q1u7bWeuVmT4K/Tg22btZr9azZ6lmz1bNmq2fNVu+ZsmbNGuyiNn4+4+Sl3FlrvXXTJvAMZM1Wz5qtjvVaPWu2etZs9awZG8n9tDrWa/Ws2epZs9WzZqtnzVavD2u2nt/xAwAAAMAlTOMHAAAAoKc2u/Fzxyaf/5nImq2eNVsd67V61mz1rNnqWTM2kvtpdazX6lmz1bNmq2fNVs+ard4zfs029Xf8AAAAAHDhbPY7fgAAAAC4QDal8VNKeVUp5Z5Syr2llO/cjDlc6kopbyqlHC2lfOgpH9tVSvmdUsrHV/5/xWbO8VJTSjlUSvm9UsrdpZQPl1K+deXj1q2hlDJTSnlvKeXPV9bs3698/PpSyntW1uznSilTmz3XS0kpZVhKuauU8vaVv1uvDqWU+0opHyyl/Fkp5c6Vj/ncbCil7Cyl/Hwp5aMr/6a91HqxEdRg3dRgq6cGWz012NqowVZH/bV6fa3BLnrjp5QyjIgfjYgvi4jnRcRrSynPu9jzeAZ4c0S86m987Dsj4h211mdHxDtW/s5fWY6I19danxsRL4mIb165t6xb29mIeEWt9YURcUtEvKqU8pKI+IGI+JGVNTsWEd+wiXO8FH1rRNz9lL9br/PzRbXWW56yHabPzbY3RMRv1VqfExEvjHP3m/ViXdRg5+3NoQZbLTXY6qnB1kYNtnrqr9XpZQ22Ge/4uS0i7q21frLWuhgRPxsRX7EJ87ik1Vr/MCKe+Bsf/oqIeMvKn98SEV95USd1iau1Plxrff/Kn0/GuU/SA2Hdmuo5p1b+OrnyX42IV0TEz6983Jo9RSnlYET8nYj4iZW/l7Bea+Vz82mUUrZHxMsi4o0REbXWxVrrk2G9WD812HlQg62eGmz11GCrpwbbMD4vG/pcg21G4+dARDzwlL8fXvkY3fbVWh+OOPcCGxF7N3k+l6xSynUR8aKIeE9Yt9TKW2b/LCKORsTvRMQnIuLJWuvyykN8jv51/29EfHtEjFf+vjus1/moEfHbpZQ/LaXcvvIxn5tP74aIeDQifnLl7ew/UUrZEtaL9VODrZ3Pv/OkBjt/arBVU4OtnvprdXpbg21G46c8zcdsLcaGKaVsjYhfiIhvq7We2Oz5XOpqraNa6y0RcTDOfTf4uU/3sIs7q0tTKeU1EXG01vqnT/3w0zzUen2mz6+1fnac+xGTby6lvGyzJ3QJm4iIz46IH6u1vigiTscz8C3FXJL8e8UFpQZbHTXY+VODrZn6a3V6W4NtRuPncEQcesrfD0bEQ5swj2eiI6WU/RERK/8/usnzueSUUibjXMHx07XWX1z5sHU7DytvY/z9OPez+TtLKRMrkc/Rv/L5EfHlpZT74tyPSLwizn33yXp1qLU+tPL/oxHxS3GuwPW5+fQOR8ThWut7Vv7+83GuCLFerJcabO18/nVQg62dGuy8qMHWQP21ar2twTaj8fO+iHj2ym9gn4qIvx8Rv7oJ83gm+tWIeN3Kn18XEb+yiXO55Kz8nO8bI+LuWusPPyWybg2llCtLKTtX/jwbEX87zv1c/u9FxFevPMyarai1flet9WCt9bo492/XO2ut/zCsV6qUsqWUsu0v/hwRr4yID4XPzadVa30kIh4opdy88qEvjoiPhPVi/dRga+fzL6EGWz012OqowVZP/bV6fa7BSq0X/91wpZRXx7kO7TAi3lRr/b6LPolLXCnlrRHx8ojYExFHIuJ7IuKXI+JtEXFNRHw6Ir6m1vo3f/ngZauU8gUR8UcR8cH4q5/9/e449zPm1u1plFI+K879grJhnGsEv63W+h9KKTfEue+m7IqIuyLiH9Vaz27eTC89pZSXR8S/rrW+xnrlVtbnl1b+OhERP1Nr/b5Syu7wufm0Sim3xLlfXjkVEZ+MiK+Plc/RsF6sgxqsmxps9dRgq6cGWzs12PlRf61NX2uwTWn8AAAAAHDhbcaPegEAAABwEWj8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPSUxg8AAABAT2n8AAAAAPTURNcDSilviojXRMTRWusLVj727yLin0XEoysP++5a6290HWvXrkE9cHDYzI8sb29m10yeTo/9wZN70nxwpjSz2o7OmR2nca358C3Ti83s9MJ0OrYMOg6eqIt5X6/klxWRnHqwlA+9Zv/RNL9/fneaj8+275OueXflO3edamaL4/Z5IyJOnZrNz509Xeuc93iync0c7XhCasfBu27iNF7P2I6hXfPqPsKaImDtFuJ0LNazXa+sAABcJJ2Nn4h4c0T854j4qb/x8R+ptf7gak524OAwfvHX2w2aNzz6Rc3s/7v6femxb/jdf5Lmcx+ZaWajqXRojJ6bN53G47zBcut19zez9957XTp2YmqU5lljaHR4Lh07TJphERHDpG6fezj/qvkN3/2jaf4vPvAP03z+Yzub2cR8x7zPpHF85Wv/qJl9+swV6dg/ftfz0nwiWdNhx7wnOuY9f6DdvLn5Pz+cDz6zkMZ1eTkfP0oaR6P8Hu1s3iTja8exY9zVdGrPu3aN7dLVTEvH6jrRX++p79jsKQAA8BSdP+pVa/3DiHjiIswFAAAAgA20nt/x8y2llA+UUt5USsnfJgEAAADARbfWxs+PRcSNEXFLRDwcET/UemAp5fZSyp2llDufeGIdPxoBAAAAwKqsqfFTaz1Sax3VWscR8V8j4rbksXfUWm+ttd66a5dNxAAAAAAuljV1Ykop+5/y16+KiA9tzHQAAAAA2Cjns537WyPi5RGxp5RyOCK+JyJeXkq5Jc5tiHxfRPzzCzhHAAAAANags/FTa33t03z4jRdgLgAAAABsIL90BwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnNH4AAAAAekrjBwAAAKCnOhs/pZQ3lVKOllI+9JSP7Sql/E4p5eMr/7/iwk4TAAAAgNU6n3f8vPn/b+/uQi0ryziA/x9mksIKG0spx75ALC/SYpDCiLKSsSK7KEgKJIK5KSgowrqJgqBu+riIQMryoi+xTOmiEivsIqwxDS2LTCyHMUeZoo+LxHy62Es8jWcc99l7zt7z9vvBsNb77nXOeng468D5z1rvSrL3iLnLk9zY3WcluXEaAwAAALBGjhn8dPdNSQ4fMX1Jkqum/auSvG3JdQEAAACwoK2u8XN6d9+XJNP2tOWVBAAAAMAyHPfFnatqX1Xtr6r9hw8/crxPBwAAAMBkq8HP/VX13CSZtoeOdmB3X9Hde7p7z65dXiIGAAAAsF22msRcn+Syaf+yJNctpxwAAAAAluXJvM79m0l+nuTsqjpQVe9N8ukkb6yqPyR54zQGAAAAYI3sPNYB3X3pUT56/ZJrAQAAAGCJLLoDAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKB2LvLFVXVPkn8k+U+Sh7t7zzKKAgAAAGBxCwU/k9d194NL+D4AAAAALJFHvQAAAAAGtWjw00l+VFW3VNW+ZRQEAAAAwHIs+qjXBd19sKpOS3JDVf2uu2/aeMAUCO1Lkued4QYjAAAAgO2yUBLT3Qen7aEk1yY5f5NjrujuPd29Z9cuwQ8AAADAdtlyElNVJ1fVMx7dT3JRkjuWVRgAAAAAi1nkUa/Tk1xbVY9+n2909w+WUhUAAAAAC9ty8NPddyc5d4m1AAAAALBEFt0BAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAP4SbkAAABdlJREFUAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAY1ELBT1XtrarfV9VdVXX5sooCAAAAYHFbDn6qakeSLya5OMk5SS6tqnOWVRgAAAAAi1nkjp/zk9zV3Xd390NJvpXkkuWUBQAAAMCiFgl+zkhy74bxgWkOAAAAgDWwSPBTm8z14w6q2ldV+6tq/+HDjyxwOgAAAADmsUjwcyDJmRvGu5McPPKg7r6iu/d0955du7xEDAAAAGC7LJLE/DLJWVX1oqo6Kck7k1y/nLIAAAAAWNTOrX5hdz9cVe9P8sMkO5Jc2d2/WVplAAAAACykuh+3LM/xO1nVA0n+tGHq2Uke3LYCxqBn89Oz+ejX/PRsfno2vxOlZy/o7uesuggAAGa2Nfh53Mmr9nf3npUVcALSs/np2Xz0a356Nj89m5+eAQCwFVZbBgAAABiU4AcAAABgUKsOfq5Y8flPRHo2Pz2bj37NT8/mp2fz0zMAAOa20jV+AAAAADh+Vn3HDwAAAADHyUqCn6raW1W/r6q7quryVdSw7qrqyqo6VFV3bJjbVVU3VNUfpu2zVlnjuqmqM6vqJ1V1Z1X9pqo+MM3r21FU1VOr6hdV9eupZ5+Y5l9UVTdPPft2VZ206lrXSVXtqKpbq+r701i/jqGq7qmq26vqtqraP825No+iqk6pqmuq6nfT77RX6RcAAFux7cFPVe1I8sUkFyc5J8mlVXXOdtdxAvhakr1HzF2e5MbuPivJjdOYxzyc5EPd/dIkr0zyvulnS9+O7t9JLuzuc5Ocl2RvVb0yyWeSfG7q2V+TvHeFNa6jDyS5c8NYv56c13X3eRteSe7aPLovJPlBd78kybmZ/bzpFwAAc1vFHT/nJ7mru+/u7oeSfCvJJSuoY611901JDh8xfUmSq6b9q5K8bVuLWnPdfV93/2ra/0dmfyidEX07qp755zR8yvSvk1yY5JppXs82qKrdSd6c5MvTuKJfW+Xa3ERVPTPJa5J8JUm6+6Hu/lv0CwCALVhF8HNGkns3jA9Mcxzb6d19XzILOZKctuJ61lZVvTDJy5PcHH17QtNjS7clOZTkhiR/TPK37n54OsQ1+r8+n+QjSR6ZxqdGv56MTvKjqrqlqvZNc67Nzb04yQNJvjo9Uvjlqjo5+gUAwBasIvipTea8WoylqaqnJ/lOkg92999XXc+66+7/dPd5SXZndkfeSzc7bHurWk9V9ZYkh7r7lo3TmxyqX493QXe/IrPHfN9XVa9ZdUFrbGeSVyT5Une/PMm/4rEuAAC2aBXBz4EkZ24Y705ycAV1nIjur6rnJsm0PbTietZOVT0ls9Dn69393Wla356E6VGSn2a2PtIpVbVz+sg1+pgLkry1qu7J7DHVCzO7A0i/jqG7D07bQ0muzSxkdG1u7kCSA9198zS+JrMgSL8AAJjbKoKfXyY5a3oLzklJ3pnk+hXUcSK6Psll0/5lSa5bYS1rZ1pr5StJ7uzuz274SN+OoqqeU1WnTPtPS/KGzNZG+kmSt0+H6dmkuz/a3bu7+4WZ/e76cXe/K/r1hKrq5Kp6xqP7SS5Kckdcm5vq7r8kubeqzp6mXp/kt9EvAAC2oLq3/4mEqnpTZv9LviPJld39qW0vYs1V1TeTvDbJs5Pcn+TjSb6X5Ookz0/y5yTv6O4jF4D+v1VVr07ysyS357H1Vz6W2To/+raJqnpZZovE7sgsCL66uz9ZVS/O7I6WXUluTfLu7v736ipdP1X12iQf7u636NcTm/pz7TTcmeQb3f2pqjo1rs1NVdV5mS0gflKSu5O8J9M1Gv0CAGAOKwl+AAAAADj+VvGoFwAAAADbQPADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACD+i8AzC/GR8fBDwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.24924596 0.21817988 0.25937003 0.18197538 0.1499188  0.15445325\n",
+      " 0.1865774  0.18403865 0.21110804 0.1810631 ]\n",
+      "[0.39094803 0.3826857  0.42081073 0.310759   0.26877594 0.29662067\n",
+      " 0.31458375 0.28709355 0.32006946 0.31328964]\n",
+      "-7.690888404846191\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.11618116 0.12725231 0.11785749 0.11394475 0.13936163 0.16115992\n",
+      " 0.14957838 0.14467596 0.26056308 0.25581202]\n",
+      "[0.12291217 0.13498841 0.12399106 0.11612049 0.14566804 0.19059217\n",
+      " 0.13759443 0.16789657 0.40931934 0.36659285]\n",
+      "-7.386691093444824\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.11106419 0.12184513 0.1242656  0.11772051 0.1396405  0.10480163\n",
+      " 0.23085348 0.25416294 0.14924178 0.25725433]\n",
+      "[0.1180159  0.15416418 0.19324291 0.15995802 0.15333365 0.10504331\n",
+      " 0.36717016 0.35351685 0.28936982 0.44242594]\n",
+      "-7.509565830230713\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.1173203  0.12589583 0.11884658 0.11641216 0.10706127 0.10628294\n",
+      " 0.09222785 0.12760282 0.25108883 0.2099084 ]\n",
+      "[0.12107199 0.1288837  0.13150896 0.11688782 0.11920497 0.1424557\n",
+      " 0.09090053 0.18093644 0.35476166 0.32838637]\n",
+      "-7.472201347351074\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.26079717 0.22089757 0.24094748 0.19556227 0.16137114 0.18125078\n",
+      " 0.21847495 0.16686511 0.15775834 0.15562241]\n",
+      "[0.4122898  0.3622304  0.34319228 0.35109726 0.3241457  0.35493872\n",
+      " 0.37524083 0.25103363 0.28793812 0.2643313 ]\n",
+      "-10.234785079956055\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.2732448  0.20127141 0.25172657 0.26581874 0.22039439 0.20176509\n",
+      " 0.1703768  0.21989661 0.1624977  0.23636822]\n",
+      "[0.42144594 0.32718015 0.41056788 0.45205054 0.4046492  0.33175963\n",
+      " 0.25695106 0.37445086 0.2606233  0.39401093]\n",
+      "-6.63955020904541\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.26074433 0.30420613 0.30364674 0.23346971 0.24678393 0.20835143\n",
+      " 0.24160685 0.25696212 0.19046707 0.17385216]\n",
+      "[0.3686048  0.3855925  0.328798   0.31058434 0.3016686  0.23996003\n",
+      " 0.34974387 0.39639434 0.26078996 0.22851513]\n",
+      "-6.532806873321533\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.2594707  0.29811907 0.29998654 0.26547143 0.25836605 0.22514705\n",
+      " 0.24415883 0.23195094 0.22564584 0.25496554]\n",
+      "[0.39776087 0.46294865 0.3459194  0.3746728  0.44063407 0.37173223\n",
+      " 0.3697653  0.3714059  0.38059473 0.38275072]\n",
+      "-7.017090797424316\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5hxAKZvbHZvZKM7vYzF4fQrh4qccDAABAGnMwAABwNtr5q17XmNneGOOjMca6mX3azH5seS4LAAAA/397dx8r2V3eB/z7eNeLjcEYv1Fkm7fKJVhpMHSLQI6QMS8yBMU0SipQolopqqMKVJCCIod/aCJRNVIbglRE5YKDI4EJcuJgRRVgGZDzR+VgAgkmBmEsg7c2XvPiYoPZZddP/7gH+WLvnLtz792du798PtJq5vzOmXue++yc2d98d86ZBczBAICjtpXg57wk965b3jeNAQBw7JiDAQBHbf4kxnlHOnfsSSfSVdVVSa5KklPy1C3sDgCAbGYOtucZx7omAGCH2sonfvYluWDd8vlJ7nviRt19TXfv7e69J+cpW9gdAADZzBzs5PmLKAMA49pK8POFJBdW1fOrak+SNye5aXvKAgBgAXMwAOCobfpUr+4+VFVvT/LprH2V6LXd/dVtqwwAgCcxBwMAllHd899vv607q3owybfWDZ2d5LvHrYAx6Nny9Gw5+rU8PVueni3vROnZc7v7nFUXwc8zB9sy/Vqeni1Pz5anZ8vTs+WdKD1bOAc7rsHPk3ZedXt3711ZAScgPVueni1Hv5anZ8vTs+XpGdvJ82k5+rU8PVueni1Pz5anZ8sboWdbucYPAAAAADuY4AcAAABgUKsOfq5Z8f5PRHq2PD1bjn4tT8+Wp2fL0zO2k+fTcvRreXq2PD1bnp4tT8+Wd8L3bKXX+AEAAADg2Fn1J34AAAAAOEZWEvxU1eVV9fWququqrl5FDTtdVV1bVfur6o51Y2dW1c1V9Y3p9pmrrHGnqaoLqupzVXVnVX21qt4xjevbAlV1SlX9bVX9/dSzP5jGn19Vt009+/Oq2rPqWneSqtpVVV+qqr+elvVrA1V1T1V9paq+XFW3T2OOzQWq6oyquqGqvja9pr1Cv9gO5mAbMwdbnjnY8szBNsccbDnmX8sbdQ523IOfqtqV5ANJXp/koiRvqaqLjncdJ4CPJLn8CWNXJ7mluy9Mcsu0zOMOJfnd7n5Rkpcnedv03NK3xQ4kuay7X5zk4iSXV9XLk/xRkvdNPftBkreusMad6B1J7ly3rF9H51XdffG6r8N0bC72/iSf6u5fSPLirD3f9IstMQc7ah+JOdiyzMGWZw62OeZgyzP/Ws6Qc7BVfOLnZUnu6u67u/tgko8nuWIFdexo3X1rku8/YfiKJNdN969L8qbjWtQO1933d/ffTfcfztpBel70baFe88i0ePL0p5NcluSGaVzP1qmq85P8SpIPTcsV/dosx+YRVNXpSV6Z5MNJ0t0Hu/uh6BdbZw52FMzBlmcOtjxzsOWZg20bx+UCI8/BVhH8nJfk3nXL+6YxNvas7r4/WfsHNsm5K65nx6qq5yV5SZLbom+zpo/MfjnJ/iQ3J/lmkoe6+9C0iWP05/1Jkt9L8ti0fFb062h0ks9U1Rer6qppzLF5ZC9I8mCSP50+zv6hqjot+sXWmYNtnuPvKJmDHT1zsKWZgy3P/Gs5w87BVhH81BHGfLUY26aqnpbkL5K8s7t/uOp6drruPtzdFyc5P2v/G/yiI212fKvamarqjUn2d/cX1w8fYVP9erJLuvulWTvF5G1V9cpVF7SD7U7y0iQf7O6XJPlRTsCPFLMjeb3imDIHW4452NEzB9s086/lDDsHW0Xwsy/JBeuWz09y3wrqOBE9UFXPTpLpdv+K69lxqurkrE04PtrdfzkN69tRmD7G+PmsnZt/RlXtnlY5Rh93SZJfrap7snaKxGVZ+98n/dpAd9833e5PcmPWJriOzSPbl2Rfd982Ld+QtUmIfrFV5mCb5/jbgDnY5pmDHRVzsE0w/1rasHOwVQQ/X0hy4XQF9j1J3pzkphXUcSK6KcmV0/0rk3xyhbXsONN5vh9Ocmd3//G6Vfq2QFWdU1VnTPdPTfKarJ2X/7kkvz5tpmeT7v797j6/u5+Xtdeuz3b3b0a/ZlXVaVX19J/dT/K6JHfEsXlE3f2dJPdW1QunoVcn+cfoF1tnDrZ5jr8Z5mDLMwdbjjnY8sy/ljfyHKy6j/+n4arqDVlLaHcluba733vci9jhqur6JJcmOTvJA0nek+SvknwiyXOSfDvJb3T3Ey8++E9WVf1ykr9J8pU8fu7vu7N2jrm+HUFV/VLWLlC2K2tB8Ce6+w+r6gVZ+9+UM5N8KclvdfeB1VW681TVpUne1d1v1K95U39unBZ3J/lYd7+3qs6KY/OIqurirF28ck+Su5P8dqZjNPrFFpiDbcwcbHnmYMszB9s8c7CjY/61OaPOwVYS/AAAAABw7K3iVC8AAAAAjgPBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMSvADAAAAMCjBDwAAAMCgBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADAowQ8AAADAoAQ/AAAAAIMS/AAAAAAMavdGG1TVtUnemGR/d//iNPafk/yHJA9Om727u//3Rj9rz65T+9STn7F4g+7F6w4dnv3Z3Y9ttPvFjz39qbPrD529wc/+0a7Z1bt+svj3OnTmzO+cpA/OZ3P1lMV9OXX3T2cf++jDp8yuP+ngzH7ny05t/q8jSfLT02d2cLhmH/ucM747u37XTHE/PHzq7GNP2uAXf/CHpy9+7Pxfx2y/k/men3Rg/vjIj38yu/rABafNrj/vGd9fuO7Mk+b3/a2fzv/sc3c/vHDdT3v+2PreoafNrj94ePHjDx6Yf/k76eD886wOza7OXFse2+CV95nnLO5JknzvJ4t72gc2eM3Y4Ng8aYPfa/axGz3HD88fPz996uKe7555HU2S2uBnHzx3pq6H53u2lZ7UY/N1PbZ7/nk2dwg8Nn94bPh33Rs8D/vkxbXvfmRx3Qce/n4O/eRH878YAADHzYbBT5KPJPkfSf7sCePv6+7/tszOTj35GXnFc/7dwvV1YPG7hse+t/iNZ5L0wQ3eNc949JUvnV3/vX//o/kfcNtMmJXkzK8vftfwnTfPvyF/7P/Oh1J7nvvIwnW/+M/un33sP3z2X8yuP23f4nUbvcHb88gG7zhq/j3Bfa9Z/K5590PzT9v3/9q1s+vPOOnHC9d9+uF/OfvYp+46MLv+f9782oXrTtk//+by6ffO92zuje/T7pl/jvbtd8yuv+tdL59d/1/ecP3Cdf/2af9v9rG/s+8Vs+vfee4tC9fde2j+2Pqz/ZfMrt/3yBkL133rrpkkIMlp355/np3yvfk39Kf8YPHf54/PmX8u/Np//Nzs+o9+/V8vXHfom/Nh2O5H54+9p35n8e/VG7yVP+2B+efwUx6af+F44F8tDqPP/Np8+vKUH8z/G/Ct/7S4ttM+Px9OnrrB3/VcuLPrwPxjf3LGfHpz8BkzAcvip/favuf/ecmBs+ZrO/TsxT0969Y9C9d97ZPvm98xAADH1YanenX3rUnmUxcAAAAAdpytXOPn7VX1D1V1bVU9c9sqAgAAAGBbbDb4+WCSf57k4iT3J/nvizasqquq6vaquv3g4Uc3uTsAAAAAlrWp4Ke7H+juw712ReX/leRlM9te0917u3vvnl3zF84FAAAAYPtsKvipqmevW/w3SeavHAsAAADAcXc0X+d+fZJLk5xdVfuSvCfJpVV1cZJOck+S3zmGNQIAAACwCRsGP939liMMf/gY1AIAAADANtrKt3oBAAAAsIMJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQW0Y/FTVtVW1v6ruWDd2ZlXdXFXfmG6feWzLBAAAAGBZR/OJn48kufwJY1cnuaW7L0xyy7QMAAAAwA6yYfDT3bcm+f4Thq9Ict10/7okb9rmugAAAADYos1e4+dZ3X1/kky3525fSQAAAABsh2N+ceequqqqbq+q2w8efvRY7w4AAACAyWaDnweq6tlJMt3uX7Rhd1/T3Xu7e++eXaducncAAAAALGuzwc9NSa6c7l+Z5JPbUw4AAAAA2+Vovs79+iT/J8kLq2pfVb01yX9N8tqq+kaS107LAAAAAOwguzfaoLvfsmDVq7e5FgAAAAC20TG/uDMAAAAAqyH4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGNTurTy4qu5J8nCSw0kOdffe7SgKAAAAgK3bUvAzeVV3f3cbfg4AAAAA28ipXgAAAACD2mrw00k+U1VfrKqrtqMgAAAAALbHVk/1uqS776uqc5PcXFVf6+5b128wBUJXJckpu0/f4u4AAAAAOFpb+sRPd9833e5PcmOSlx1hm2u6e293792z69St7A4AAACAJWw6+Kmq06rq6T+7n+R1Se7YrsIAAAAA2JqtnOr1rCQ3VtXPfs7HuvtT21IVAAAAAFu26eCnu+9O8uJtrAUAAACAbeTr3AEAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAYl+AEAAAAYlOAHAAAAYFCCHwAAAIBBCX4AAAAABiX4AQAAABiU4AcAAABgUIIfAAAAgEEJfgAAAAAGJfgBAAAAGJTgBwAAAGBQgh8AAACAQQl+AAAAAAa1peCnqi6vqq9X1V1VdfV2FQUAAADA1m06+KmqXUk+kOT1SS5K8paqumi7CgMAAABga7byiZ+XJbmru+/u7oNJPp7kiu0pCwAAAICt2krwc16Se9ct75vGAAAAANgBthL81BHG+kkbVV1VVbdX1e0HDz+6hd0BAAAAsIytBD/7klywbvn8JPc9caPuvqa793b33j27Tt3C7gAAAABYxlaCny8kubCqnl9Ve5K8OclN21MWAAAAAFu1e7MP7O5DVfX2JJ9OsivJtd391W2rDAAAAIAtqe4nXZbn2O2s6sEk31o3dHaS7x63AsagZ8vTs+Xo1/L0bHl6trwTpWfP7e5zVl0EAABrjmvw86SdV93e3XtXVsAJSM+Wp2fL0a/l6dny9Gx5egYAwGZs5Ro/AAAAAOxggh8AAACAQa06+Llmxfs/EenZ8vRsOfq1PD1bnp4tT88AAFjaSq/xAwAAAMCxs+pP/AAAAABwjKwk+Kmqy6vq61V1V1VdvYoadrqquraq9lfVHevGzqyqm6vqG9PtM1dZ405TVRdU1eeq6s6q+mpVvWMa17cFquqUqvrbqvr7qWd/MI0/v6pum3r251W1Z9W17iRVtauqvlRVfz0t69cGquqeqvpKVX25qm6fxhybC1TVGVV1Q1V9bXpNe4V+AQCwGcc9+KmqXUk+kOT1SS5K8paquuh413EC+EiSy58wdnWSW7r7wiS3TMs87lCS3+3uFyV5eZK3Tc8tfVvsQJLLuvvFSS5OcnlVvTzJHyV539SzHyR56wpr3InekeTOdcv6dXRe1d0Xr/tKcsfmYu9P8qnu/oUkL87a802/AABY2io+8fOyJHd1993dfTDJx5NcsYI6drTuvjXJ958wfEWS66b71yV503Etaofr7vu7+++m+w9n7Y3SedG3hXrNI9PiydOfTnJZkhumcT1bp6rOT/IrST40LVf0a7Mcm0dQVacneWWSDydJdx/s7oeiXwAAbMIqgp/zkty7bnnfNMbGntXd9ydrIUeSc1dcz45VVc9L8pIkt0XfZk2nLX05yf4kNyf5ZpKHuvvQtIlj9Of9SZLfS/LYtHxW9OtodJLPVNUXq+qqacyxeWQvSPJgkj+dTin8UFWdFv0CAGATVhH81BHGfLUY26aqnpbkL5K8s7t/uOp6drruPtzdFyc5P2ufyHvRkTY7vlXtTFX1xiT7u/uL64ePsKl+Pdkl3f3SrJ3m+7aqeuWqC9rBdid5aZIPdvdLkvwoTusCAGCTVhH87Etywbrl85Pct4I6TkQPVNWzk2S63b/ienacqjo5a6HPR7v7L6dhfTsK06kkn8/a9ZHOqKrd0yrH6OMuSfKrVXVP1k5TvSxrnwDSrw10933T7f4kN2YtZHRsHtm+JPu6+7Zp+YasBUH6BQDA0lYR/HwhyYXTt+DsSfLmJDetoI4T0U1JrpzuX5nkkyusZceZrrXy4SR3dvcfr1ulbwtU1TlVdcZ0/9Qkr8natZE+l+TXp830bNLdv9/d53f387L22vXZ7v7N6Nesqjqtqp7+s/tJXpfkjjg2j6i7v5Pk3qp64TT06iT/GP0CAGATqvv4n5FQVW/I2v+S70pybXe/97gXscNV1fVJLk1ydpIHkrwnyV8l+USS5yT5dpLf6O4nXgD6n6yq+uUkf5PkK3n8+ivvztp1fvTtCKrql7J2kdhdWQuCP9Hdf1hVL8jaJ1rOTPKlJL/V3QdWV+nOU1WXJnlXd79Rv+ZN/blxWtyd5GPd/d6qOiuOzSOqqouzdgHxPUnuTvLbmY7R6BcAAEtYSfADAAAAwLG3ilO9AAAAADgOBD8AAAAAgxL8AAAAAAxK8AMAAAAwKMEPAAAAwKAEPwAAAACDEvwAAAAADErwAwAAADCo/w8WcIcidqrIsgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.11152558 0.10796538 0.09854074 0.09548028 0.09576764 0.13426125\n",
+      " 0.20254236 0.16299719 0.1362484  0.19893768]\n",
+      "[0.10802491 0.12525348 0.09468871 0.08889368 0.10068989 0.13528737\n",
+      " 0.27284318 0.20962347 0.15593511 0.2929741 ]\n",
+      "-7.402263164520264\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAgwQrAAAAAIMEKwAAAACDBCsAAAAAg7ZNK6iqa5JclORgd//ApO3fJ/lXSb42KXtHd9+4wXMvSPJfkqwkeW93//Isg1o5dUdv2/XMqXUnbz88teakAyuz7DK1e3pf33p0+0x9ZduTU0tOOqln6urJw7NlX/Xtmlrz/NMfmKmvU2r6XOyo6ftLki88Mv33mCTbTpo+Z4/NOP81vavUt2fqKpntn5me4dc0y7iS5MmTp9fsPm223+WDh58+teaxJ6a+DSRJDj88w8CS1NOfmF708GyvyyefNv11Uo/N9kuq2V5yM9n2yGydnXToWzMUzZhvr0yve+z02X5HvX3G8X9r+tzO+nc9i2fsenSmukMPTf+7TpKa4U9xVrO8xnu2l9Kmztkp9z82W+GT03faPeOLZNa6Kb6Vh/N4z/gCBgBYYjXtg1RV/YMkh5L81rpg5VB3/6fv8LyVJH+W5LVJ9ie5Jcml3f2FaYM65UVn9nPf+a+nDn73mdO/XD7jXadOrUmSbb/0tak1X7r9BTP1Vc+a/kF3x44ZvnAleehrO2eq2/6X0z/Rv+sff3Cmvs7efv/Umr9/ymwhxw/u+4mZ6p614+GpNXd/YfdMfW17ePo3oKf/5Wyf5Wf5MpUk357h17Ttkdn6evS5078A/dIbrpuprz/4xsun1nz5wTNm6uvr+54zU93JL31oak3f8l0z9fXI35v+WnravafM1NdJj89UlpNm+DJ+xu2zdfa0T981taZ27pipr5w6ve7en3jeTF09+oLZksWdd08Pamb9u64np38Zf8U/u2Omvv7PR182U9326X+KM7/GZ8go89iu2RKTkw9tXpbwPb9+z0x1/fD099j+9vRQPUn6iRleJE9Or7m5P56H+gHBCgBwzJv6kbK7P5lktv8e/9vOTXJPd3+lux9P8sEkFw/0AwAAALCU5rnGyluq6nNVdU1VbXS+x+4kX12zvn/SBgAAAHBcGA1WfjPJ9yQ5J8l9Sf7zBjUbHd57xGPBq2pvVe2rqn1PHJp+yDIAAADAog0FK919f3c/0d1PJvnvWT3tZ739Sc5as35mkgPfoc+runtPd+9ZmfWaAwAAAAALNBSsVNXaKyT+eJI7Nyi7JcnZVfXiqtqe5JIkN4zsDwAAAGAZzXK75euSnJ/kjKran+TyJOdX1TlZPbXn3iQ/Pal9flZvq3xhdx+uqrck+WhWb7d8TXd//qj8KwAAAAAWYGqw0t2XbtB89RFqDyS5cM36jUluHB4dAAAAwBKb565AAAAAACc0wQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwKCpwUpVXVNVB6vqzjVt/7GqvlhVn6uq66vqtCM8996quqOqbquqfZs5cAAAAIBFm+WIlfcluWBd201JfqC7X5bkz5L84nd4/qu6+5zu3jM2RAAAAIDlNDVY6e5PJnlgXdvHuvvwZPVPkpx5FMYGAAAAsNQ24xor/yLJR46wrZN8rKo+U1V7N2FfAAAAAEtj2zxPrqp/l+RwkvcfoeS87j5QVc9OclNVfXFyBMxGfe1NsjdJVnZteMkWAAAAgKUyfMRKVV2W5KIkP9ndvVFNdx+YPB5Mcn2Sc4/UX3df1d17unvPys4do8MCAAAA2DJDwUpVXZDk3yZ5Q3c/coSaHVV16lPLSV6X5M6NagEAAACORbPcbvm6JH+c5CVVtb+q3pzkiiSnZvX0ntuq6spJ7fOr6sbJU5+T5NNVdXuSP03yv7r7D4/KvwIAAABgAaZeY6W7L92g+eoj1B5IcuFk+StJXj7X6AAAAACW2GbcFQgAAADghCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYNFOwUlXXVNXBqrpzTdvpVXVTVd09eXzmEZ572aTm7qq6bLMGDgAAALBosx6x8r4kF6xre3uSj3f32Uk+Pln/W6rq9CSXJ/mhJOcmufxIAQwAAADAsWamYKW7P5nkgXXNFye5drJ8bZIf2+CpP5rkpu5+oLu/meSm/P8BDQAAAMAxaZ5rrDynu+9Lksnjszeo2Z3kq2vW90/aAAAAAI55R/vitbVBW29YWLW3qvZV1b4nDj18lIcFAAAAML95gpX7q+p5STJ5PLhBzf4kZ61ZPzPJgY066+6runtPd+9Z2bljjmEBAAAAbI15gpUbkjx1l5/Lkvz+BjUfTfK6qnrm5KK1r5u0AQAAABzzZr3d8nVJ/jjJS6pqf1W9OckvJ3ltVd2d5LWT9VTVnqp6b5J09wNJ/kOSWyY/7560AQAAABzzts1S1N2XHmHTazao3ZfkX65ZvybJNUOjAwAAAFhiR/vitQAAAADHLcEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMAgwQoAAADAIMEKAAAAwCDBCgAAAMCg4WClql5SVbet+Xmoqt62rub8qnpwTc075x8yAAAAwHLYNvrE7v5SknOSpKpWkvzfJNdvUPqp7r5odD8AAAAAy2qzTgV6TZIvd/efb1J/AAAAAEtvs4KVS5Jcd4RtP1xVt1fVR6rq+zdpfwAAAAALN3ewUlXbk7whyf/cYPOtSV7Y3S9P8t+SfPg79LO3qvZV1b4nDj0877AAAAAAjrrNOGLl9Ulu7e7712/o7oe6+9Bk+cYkJ1fVGRt10t1Xdfee7t6zsnPHJgwLAAAA4OjajGDl0hzhNKCqem5V1WT53Mn+vrEJ+wQAAABYuOG7AiVJVT0jyWuT/PSatp9Jku6+Mskbk/xsVR1O8miSS7q759knAAAAwLKYK1jp7keS7FrXduWa5SuSXDHPPgAAAACW1WbdFQgAAADghCNYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYNHewUlX3VtUdVXVbVe3bYHtV1X+tqnuq6nNV9YPz7hMAAABgGWzbpH5e1d1fP8K21yc5e/LzQ0l+c/IIAAAAcEzbilOBLk7yW73qT5KcVlXP24L9AgAAABxVmxGsdJKPVdVnqmrvBtt3J/nqmvX9kzYAAACAY9pmnAp0XncfqKpnJ7mpqr7Y3Z9cs702eE6vb5iEMnuTZGXXaZswLAAAAICja+4jVrr7wOTxYJLrk5y7rmR/krPWrJ+Z5MAG/VzV3Xu6e8/Kzh3zDgsAAADgqJsrWKmqHVV16lPLSV6X5M51ZTck+aeTuwO9MsmD3X3fPPsFAAAAWAbzngr0nCTXV9VTfX2gu/+wqn4mSbr7yiQ3JrkwyT1JHknyz+fcJwAAAMBSmCtY6e6vJHn5Bu1XrlnuJD83z34AAAAAltFW3G4ZAAAA4LgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGDQcrFTVWVX1iaq6q6o+X1Vv3aDm/Kp6sKpum/y8c77hAgAAACyPbXM893CSn+/uW6vq1CSfqaqbuvsL6+o+1d0XzbEfAAAAgKU0fMRKd9/X3bdOlv86yV1Jdm/WwAAAAACW3aZcY6WqXpTkFUlu3mDzD1fV7VX1kar6/s3YHwAAAMAymOdUoCRJVe1M8rtJ3tbdD63bfGuSF3b3oaq6MMmHk5x9hH72JtmbJCu7Tpt3WAAAAABH3VxHrFTVyVkNVd7f3b+3fnt3P9TdhybLNyY5uarO2Kiv7r6qu/d0956VnTvmGRYAAADAlpjnrkCV5Ookd3X3rx6h5rmTulTVuZP9fWN0nwAAAADLZJ5Tgc5L8k+S3FFVt03a3pHkBUnS3VcmeWOSn62qw0keTXJJd/cc+wQAAABYGsPBSnd/OklNqbkiyRWj+wAAAABYZptyVyAAAACAE5FgBQAAAGCQYAUAAABgkGAFAAAAYJBgBQAAAGCQYAUAAABgkGAFAAAAYJBgBQAAAGCQYAUAAABgkGAFAAAAYJBgBQAAAGCQYAUAAABgkGAFAAAAYJBgBQAAAGCQYAUAAABg0FzBSlVdUFVfqqp7qurtG2w/pap+e7L95qp60Tz7AwAAAFgmw8FKVa0k+Y0kr0/y0iSXVtVL15W9Ock3u/t7k/xakl8Z3R8AAADAspnniJVzk9zT3V/p7seTfDDJxetqLk5y7WT5d5K8pqpqjn0CAAAALI15gpXdSb66Zn3/pG3Dmu4+nOTBJLvm2CcAAADA0pgnWNnoyJMeqFktrNpbVfuqat8Thx6eY1gAAAAAW2OeYGV/krPWrJ+Z5MCRaqpqW5LvSvLARp1191Xdvae796zs3DHHsAAAAAC2xjzByi1Jzq6qF1fV9iSXJLlhXc0NSS6bLL8xyR9194ZHrAAAAAAca7aNPrG7D1fVW5J8NMlKkmu6+/NV9e4k+7r7hiRXJ/kfVXVPVo9UuWQzBg0AAACwDIaDlSTp7huT3Liu7Z1rlr+V5E3z7AMAAABgWdUynplTVV9L8udrms5I8vUFDQfzv2jmf3HM/WKZ/8U62vP/wu5+1lHsHwBgSyxlsLJeVe3r7j2LHseJyvwvlvlfHHO/WOZ/scw/AMBs5rl4LQAAAMAJTbACAAAAMOhYCVauWvQATnDmf7HM/+KY+8Uy/4tl/gEAZnBMXGMFAAAAYBkdK0esAAAAACydpQ9WquqCqvpSVd1TVW9f9HiOd1V1TVUdrKo717SdXlU3VdXdk8dnLnKMx6uqOquqPlFVd1XV56vqrZN2878FquppVfWnVXX7ZP7fNWl/cVXdPJn/366q7Yse6/Gqqlaq6rNV9QeTdXO/Rarq3qq6o6puq6p9kzbvPQAAM1jqYKWqVpL8RpLXJ3lpkkur6qWLHdVx731JLljX9vYkH+/us5N8fLLO5juc5Oe7+/uSvDLJz03+3s3/1ngsyau7++VJzklyQVW9MsmvJPm1yfx/M8mbFzjG491bk9y1Zt3cb61Xdfc5a26x7L0HAGAGSx2sJDk3yT3d/ZXufjzJB5NcvOAxHde6+5NJHljXfHGSayfL1yb5sS0d1Amiu+/r7lsny3+d1S+Yu2P+t0SvOjRZPXny00leneR3Ju3m/yipqjOT/KMk752sV8z9onnvAQCYwbIHK7uTfHXN+v5JG1vrOd19X7L65T/Jsxc8nuNeVb0oySuS3Bzzv2Ump6LcluRgkpuSfDnJX3X34UmJ96Cj59eT/JskT07Wd8Xcb6VO8rGq+kxV7Z20ee8BAJjBtkUPYIraoM1tjDiuVdXOJL+b5G3d/dDqf9yzFbr7iSTnVNVpSa5P8n0blW3tqI5/VXVRkoPd/ZmqOv+p5g1Kzf3Rc153H6iqZye5qaq+uOgBAQAcK5b9iJX9Sc5as35mkgMLGsuJ7P6qel6STB4PLng8x62qOjmrocr7u/v3Js3mf4t1918l+d9ZvdbNaVX1VAjtPejoOC/JG6rq3qye8vnqrB7BYu63SHcfmDwezGqoeG689wAAzGTZg5Vbkpw9uTPE9iSXJLlhwWM6Ed2Q5LLJ8mVJfn+BYzluTa4pcXWSu7r7V9dsMv9boKqeNTlSJVX19CT/MKvXuflEkjdOysz/UdDdv9jdZ3b3i7L6Pv9H3f2TMfdboqp2VNWpTy0neV2SO+O9BwBgJtW93EdWV9WFWf2fy5Uk13T3exY8pONaVV2X5PwkZyS5P8nlST6c5ENJXpDkL5K8qbvXX+CWOVXVjyT5VJI78jfXmXhHVq+zYv6Psqp6WVYv0LmS1dD5Q9397qr67qweRXF6ks8m+anufmxxIz2+TU4F+oXuvsjcb43JPF8/Wd2W5APd/Z6q2hXvPQAAUy19sAIAAACwrJb9VCAAAACApSVYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABgkWAEAAAAYJFgBAAAAGCRYAQAAABj0/wAFTBcLUPhVOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.1891198  0.21245067 0.11375898 0.21876599 0.17907904 0.19572453\n",
+      " 0.20591077 0.18838076 0.16836178 0.24888688]\n",
+      "[0.28237012 0.30047625 0.10842206 0.349882   0.2629233  0.2766327\n",
+      " 0.3261645  0.32676938 0.22456282 0.36228007]\n",
+      "-6.838672637939453\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.26525447 0.20227718 0.16936694 0.15414512 0.15350477 0.15022981\n",
+      " 0.16572466 0.15559134 0.15755858 0.16734494]\n",
+      "[0.41597676 0.32071397 0.33621624 0.26683503 0.28123468 0.25556195\n",
+      " 0.41909048 0.29799244 0.2599865  0.25557628]\n",
+      "-6.5227580070495605\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.10212699 0.09827553 0.10051721 0.09961789 0.09490699 0.09356681\n",
+      " 0.09231654 0.09108451 0.08984405 0.0885736 ]\n",
+      "[0.09293962 0.08944091 0.09424179 0.09359175 0.08664685 0.08513386\n",
+      " 0.08370514 0.08226986 0.08078754 0.07922352]\n",
+      "-8.633907318115234\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.21209855 0.16956912 0.21401809 0.27985725 0.26169622 0.23552138\n",
+      " 0.20158373 0.18696313 0.16326152 0.15223208]\n",
+      "[0.34245536 0.26235473 0.3862635  0.4097918  0.39038345 0.39045033\n",
+      " 0.29390138 0.24680321 0.21080637 0.28459427]\n",
+      "-6.249783992767334\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.25701508 0.19305713 0.15556504 0.22758085 0.19182432 0.17283928\n",
+      " 0.17952912 0.21623343 0.17581777 0.20800099]\n",
+      "[0.39073482 0.34967428 0.28198206 0.4109671  0.28411594 0.27315092\n",
+      " 0.30498514 0.3249544  0.26701003 0.28990373]\n",
+      "-8.162667274475098\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.10187121 0.20555556 0.25094697 0.25612128 0.26600394 0.27968302\n",
+      " 0.27726704 0.2748356  0.25621524 0.19317323]\n",
+      "[0.09260029 0.30920756 0.41037732 0.40864187 0.4351669  0.42533502\n",
+      " 0.41110986 0.3965467  0.33207256 0.34741166]\n",
+      "-6.729473114013672\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.24147294 0.2855732  0.2929864  0.27804467 0.27196732 0.27311295\n",
+      " 0.21022318 0.2144589  0.1784137  0.16083395]\n",
+      "[0.37896225 0.4254961  0.33391264 0.29991606 0.366262   0.32236382\n",
+      " 0.3107293  0.32707408 0.3169909  0.26289862]\n",
+      "-9.16850757598877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.18940036 0.15744455 0.1309619  0.18345915 0.18845582 0.17528039\n",
+      " 0.16476972 0.15183786 0.1481686  0.14485821]\n",
+      "[0.2514994  0.2420539  0.28831345 0.3735306  0.2972508  0.25996768\n",
+      " 0.26072228 0.22503942 0.21829061 0.24953699]\n",
+      "-7.0189642906188965\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.0964459  0.17056513 0.22313309 0.23259352 0.24475445 0.1985518\n",
+      " 0.20696415 0.2017536  0.18377674 0.18172859]\n",
+      "[0.08555546 0.21938425 0.34890383 0.3910132  0.42296827 0.3039058\n",
+      " 0.36175776 0.3145116  0.32173258 0.25138718]\n",
+      "-8.732368469238281\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.14338797 0.26642802 0.266923   0.18762957 0.27792162 0.27091458\n",
+      " 0.25049862 0.2575286  0.22108391 0.24561949]\n",
+      "[0.23022906 0.39322814 0.3868789  0.27746862 0.39362523 0.34739965\n",
+      " 0.29787534 0.3281621  0.3356835  0.34134695]\n",
+      "-6.554551601409912\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training Start!!!\")\n",
+    "iteration=1\n",
+    "for epoch in range(hp.epochs):\n",
+    "    for tiers in dataloader:\n",
+    "        \n",
+    "        lr_scheduling(optimizer, iteration, 3e-4, 10)\n",
+    "        \n",
+    "        model.zero_grad()\n",
+    "        mu, std, pi = model(tiers[1].to(device), 2)\n",
+    "\n",
+    "        if use_GMM:\n",
+    "            loss = criterion(tiers[2].to(device), mu, std, pi)\n",
+    "        else:\n",
+    "            loss = criterion(tiers[2].to(device), mu[:,:,:,0])\n",
+    "            \n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        iteration += 1\n",
+    "        \n",
+    "        if iteration%100==0:\n",
+    "            fig = plt.figure(figsize=(20,10))\n",
+    "            ax1, ax2, ax3 = fig.add_subplot(221), fig.add_subplot(222), fig.add_subplot(223)\n",
+    "            ax1.imshow(tiers[2][0].numpy(), origin='lower')\n",
+    "            ax2.imshow(mu[0,:,:,0].cpu().detach().numpy(), origin='lower')\n",
+    "            ax3.imshow(np.tile(mu[0,:,:,0][0:1].cpu().detach().numpy(), (16,1)), origin='lower')\n",
+    "            plt.show()\n",
+    "            print(mu[0,:,:,0][0].cpu().detach().numpy()[:10])\n",
+    "            print(mu[0,:,:,0][1].cpu().detach().numpy()[:10])\n",
+    "            print(loss.item())\n",
+    "    break"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:LYH] *",
+   "language": "python",
+   "name": "conda-env-LYH-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/utils/audio_processing.py b/utils/audio_processing.py
new file mode 100644
index 0000000..9d41964
--- /dev/null
+++ b/utils/audio_processing.py
@@ -0,0 +1,30 @@
+import numpy as np
+import scipy, librosa
+
+def normalize(wav):
+    return wav / np.max( np.abs(wav) )
+
+def trim(wav, threshold=0.01):
+    cut = np.where((abs(wav)>threshold))[0]
+    wav = wav[cut[0]:(cut[-1]+1)] # Trimming
+    return wav
+
+def wav_to_spec(wav, sr, n_fft, n_hop):
+    _, _, Zxx = scipy.signal.stft(wav, fs=sr, nperseg=n_fft, noverlap=n_fft-n_hop)
+    return 20 * np.log10(np.maximum(np.abs(Zxx), 1e-8))
+
+def wav_to_melspec(wav, sr, n_fft, n_hop, n_mels, mel_basis=None):
+    _, _, Zxx = scipy.signal.stft(wav, fs=sr, nperseg=n_fft, noverlap=n_fft-n_hop)
+    if mel_basis is None:
+        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
+    D = np.matmul(mel_basis, np.abs(Zxx))
+    D = 20 * np.log10(np.maximum(D, 1e-8))
+    D = np.clip((D + 100) / 100, 0, 1)
+    return D
+
+def spec_to_mel(spectrogram, sr, n_fft, n_mels, mel_basis=None):
+    S = np.power(10.0, spectrogram * 0.05)
+    if mel_basis is None:
+        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
+    D = np.matmul(mel_basis, S)
+    return 20 * np.log10(np.maximum(D, 1e-8))
\ No newline at end of file
diff --git a/utils/constant.py b/utils/constant.py
new file mode 100644
index 0000000..7a7d6ed
--- /dev/null
+++ b/utils/constant.py
@@ -0,0 +1,3 @@
+# x:y = tiers:(axis should be divisible by)
+t_div = {1:1, 2:1, 3:1, 4:2, 5:2, 6:4, 7:4}
+f_div = {1:1, 2:1, 3:2, 4:2, 5:4, 6:4, 7:8}
\ No newline at end of file
diff --git a/utils/data.py b/utils/data.py
new file mode 100644
index 0000000..c2335ed
--- /dev/null
+++ b/utils/data.py
@@ -0,0 +1,114 @@
+from torch.utils.data import Dataset, DataLoader
+import scipy, librosa
+import os
+import torch
+import random
+from utils import hparams as hp
+from .audio_processing import *
+
+
+class Spliter():
+    def __init__(self):
+        self.split=0
+    
+    def reset(self):
+        self.split=0
+        
+    def __call__(self, melspec):
+        if self.split%2==0:
+            self.split += 1
+            return melspec[0::2, :], melspec[1::2, :]
+        
+        elif self.split%2==1:
+            self.split += 1
+            return melspec[:, 0::2], melspec[:, 1::2]
+
+
+def pad_mel(melspecs):
+    B, F, T = len(melspecs), melspecs[0].shape[0], max([x.shape[1] for x in melspecs])
+    padded_mel =  np.zeros((B, F, T))
+    for i, mel in enumerate(melspecs):
+        padded_mel[i, :, :mel.shape[1]] = mel
+
+    return torch.from_numpy(padded_mel).to(torch.float)
+
+
+class MelData(Dataset):
+    def __init__(self, hp):
+        super(Dataset, self).__init__()
+        self.root_dir = hp.root_dir
+        self.n_tiers = hp.n_tiers
+        self.sr = hp.sr
+        self.n_fft = hp.n_fft
+        self.n_mels = hp.n_mels
+        self.n_hop = hp.n_hop
+        self.n_overlap = hp.n_fft - hp.n_hop
+        self.n_bucket = hp.n_bucket
+        
+        self.wav_files = list(filter(lambda f: f.endswith('.wav'), os.listdir(self.root_dir)))
+        self.split = Spliter()
+        self.mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels)
+        #self.bucket_by_sequence_length()
+        
+    def bucket_by_sequence_length(self):
+        ##### Wav -> Melspectrogram #####
+        self.wav_lengths = []
+        for wav_file in self.wav_files:
+            wav, _ = librosa.load( os.path.join(self.root_dir, wav_file) )
+            self.wav_lengths.append(len(wav))
+            
+        self.wav_length = list(zip(self.wav_files, self.wav_lengths))
+        self.wav_length.sort(key = lambda x: x[1])
+        
+        self.buckets = {}
+        bucket_size = len(self.wav_length)//self.n_bucket
+        for i in range(self.n_bucket):
+            self.buckets[i] = self.wav_length[i*bucket_size : (i+1)*bucket_size]
+            
+    def shuffle(self):
+        for i in range(self.n_bucket):
+            random.shuffle(self.buckets[i])
+        
+    def __getitem__(self, i):
+        ##### Wav -> Melspectrogram #####
+        wav, sr = librosa.load( os.path.join(self.root_dir, self.wav_files[i]) )
+        wav = normalize(wav)
+        wav = trim(wav)
+        melspec = wav_to_melspec(wav, self.sr, self.n_fft, self.n_hop, self.n_mels, self.mel_basis)
+        
+        ##### Melspectrogram Validation #####
+        n_half_t = (self.n_tiers-1) // 2
+        n_time = melspec.shape[1] - melspec.shape[1] % 2**n_half_t
+        melspec = melspec[:, :n_time]
+        
+        ##### Build mel_tiers #####
+        mel_tiers = [None] + [ 0 for _ in range(self.n_tiers) ] + [melspec]
+        
+        for t in range(self.n_tiers, 1, -1):
+            tier, melspec = self.split(melspec)
+            mel_tiers[t] = tier
+        mel_tiers[1] = melspec
+        self.split.reset()
+        
+        return mel_tiers
+    
+    def __len__(self):
+        return len(self.wav_files)
+    
+    
+
+class MelCollate():
+    def __init__(self, hp):
+        self.n_tiers = hp.n_tiers
+        
+    def __call__(self, batch):
+        mel_tiers = [None] + [ [] for _ in range(self.n_tiers+1) ]
+        
+        for data in batch:
+            for t in range(1, self.n_tiers+2):
+                mel_tiers[t].append(data[t])
+        
+        for t in range(1, self.n_tiers+2):
+            mel_tiers[t] = pad_mel(mel_tiers[t])
+        
+        return mel_tiers
\ No newline at end of file
diff --git a/utils/hparams.py b/utils/hparams.py
new file mode 100644
index 0000000..94f8195
--- /dev/null
+++ b/utils/hparams.py
@@ -0,0 +1,14 @@
+root_dir = '../dataset/KSS/wavs'
+n_tiers = 6
+sr = 22050
+n_fft = 6 * 256
+n_hop = 256
+n_mels = 256
+
+K=3
+layers=[6, 5, 4, 3, 2, 2]
+batch_size = 4
+hidden_dim = 512
+epochs=5
+use_central=True
+n_bucket=10
\ No newline at end of file
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000..a386e00
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,43 @@
+import math
+import random
+import numpy as np
+import torch
+from torch.optim.optimizer import Optimizer, required
+import librosa
+
+def lr_scheduling(opt, step, init_lr=1e-3, warmup_steps=4000):
+    opt.param_groups[0]['lr'] = init_lr * warmup_steps ** 0.5 * min(step * warmup_steps ** -1.5, step ** -0.5)
+    return
+
+
+
+def cut_wav(L, wav):
+    samples = len(wav)
+    if samples < L:
+        start = random.randint(0, L - samples)
+        # if shorter than desired segment length(3.0s), then let's pad with zero
+        wav = np.pad(wav, (start, L - samples - start), 'constant', constant_values=0.0)
+    else:
+        start = random.randint(0, samples - L)
+        wav = wav[start:start+L]
+
+    return wav
+
+
+
+def read_wav_np(wavpath):
+    os.path.join(self.root_dir, wav_file)
+    wav, sr = librosa.load(wavpath)
+    
+    if len(wav.shape) == 2:
+        wav = wav[:, 0]
+    
+    if wav.dtype == np.int16:
+        wav = wav / 32768.0
+    elif wav.dtype == np.int32:
+        wav = wav / 2147483648.0
+    elif wav.dtype == np.uint8:
+        wav = (wav - 128) / 128.0
+    
+    wav = wav.astype(np.float32)
+    return wav