text: import tensorflow as tf
  import torch
  import torch.nn as nn
  import numpy as np
  import unittest
  
  from numpy.testing import assert_allclose
  from tensorflow.keras import Model
  import tensorflow as tf
  
  const_init = np.random.rand()
  
  
  class TFConv(Model):
      def __init__(self, **kwargs):
          super(TFConv, self).__init__()
          '''[SOURCE STRUCTURE CODE STARTS HERE]'''
          self.conv_1_1 = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.pool_1 = tf.keras.layers.MaxPool2D((2, 2), strides=(2, 2))
          self.conv_2_1 = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.pool_2 = tf.keras.layers.MaxPool2D((2, 2), strides=(2, 2))
          self.conv_3_1 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.conv_3_2 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.pool_3 = tf.keras.layers.MaxPool2D((2, 2), strides=(2, 2))
          self.conv_4_1 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.conv_4_2 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.pool_4 = tf.keras.layers.MaxPool2D((2, 2), strides=(2, 2))
          self.conv_5_1 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.conv_5_2 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')
          self.act1 = tf.keras.layers.ReLU()
          self.pool_5 = tf.keras.layers.MaxPool2D((2, 2), strides=(2, 2))
          self.flat = tf.keras.layers.Flatten()
          self.dense_1 = tf.keras.layers.Dense(4096)
          self.act1 = tf.keras.layers.ReLU()
          self.dense_2 = tf.keras.layers.Dense(4096)
          self.act1 = tf.keras.layers.ReLU()
          self.dense_3 = tf.keras.layers.Dense(1000)
          self.act1 = tf.keras.layers.Softmax()
          '''[SOURCE STRUCTURE CODE ENDS HERE]'''
  
      def call(self, x):
          '''[SOURCE FORWARD-PASS CODE STARTS HERE]'''
          x = self.conv_1_1(x)
          x = self.pool_1(x)
          x = self.conv_2_1(x)
          x = self.pool_2(x)
          x = self.conv_3_1(x)
          x = self.conv_3_2(x)
          x = self.pool_3(x)
          x = self.conv_4_1(x)
          x = self.conv_4_2(x)
          x = self.pool_4(x)
          x = self.conv_5_1(x)
          x = self.conv_5_2(x)
          x = self.pool_5(x)
          x = self.flat(x)
          x = self.dense_1(x)
          x = self.dense_2(x)
          x = self.dense_3(x)
          '''[SOURCE FORWARD-PASS CODE ENDS HERE]'''
  
          return x
  
  
  class TorchConv(nn.Module):
      def __init__(self, constant_init):
          super(TorchConv, self).__init__()
          ''' Ground Truth:
          self.conv1 = nn.Conv2d(1, 32, 3, stride=2, bias=True)
  
          [GENERATED STRUCTURE CODE STARTS HERE]
          '''
  
          for param in self.parameters():
              nn.init.constant_(param.data, constant_init)
  
      def forward(self, x):
          ''' Ground Truth:
          x = x.permute(0, 3, 1, 2)
          x = self.conv1(x)  # with parameters, random init
          x = x.permute(0, 2, 3, 1)
  
          [GENERATED FORWARD-PASS CODE STARTS HERE]
          '''
  
          return x
  
  
  class TestConsistency:
      def test_structure(self):
          # for certain number of random inputs
          for i in range(10):
              # input for the network
              const_init = np.random.rand()
              # dataset-size, 50-width, 40-height, 1-channels
              imgs = np.random.rand(100, 50, 40, 1)
  
              # build the Tensorflow and PyTorch model
              tf_conv_model = TFConv(const_init)
              torch_conv_model = TorchConv(const_init)
              tf_conv_model.build(imgs.shape)  # have to build the model to count the # of parameters for tf
  
              # match the number of parameters
              tf_param_num = tf_conv_model.count_params()
              torch_param_num = sum(p.numel() for p in torch_conv_model.parameters())
              assert tf_param_num == torch_param_num
  
      def test_forward_pass(self):
          # for certain number of random inputs
          for i in range(10):
              # input for the network
              const_init = np.random.rand()
              # dataset-size, 50-width, 40-height, 1-channels
              imgs = np.random.rand(100, 50, 40, 1)
  
              # build the Tensorflow and PyTorch model
              tf_conv_model = TFConv(const_init)
              torch_conv_model = TorchConv(const_init)
  
              # get the result from Tensorflow
              tf_input = tf.convert_to_tensor(imgs)
              tf_x = tf_conv_model(tf_input)
              tf_result = tf_x.numpy()
  
              # get the result from Torch
              torch_input = torch.Tensor(imgs)
              torch_x = torch_conv_model(torch_input)
              torch_result = torch_x.detach().numpy()
  
              # check if the results are consistent
              assert_allclose(torch_result, tf_result, atol=1e-5)
  
  
  def other():
      # dataset-size, 50-width, 40-height, 1-channels
      imgs = np.random.rand(10, 10, 10, 1)
      print(imgs)
  
      # build the Tensorflow and PyTorch model
      tf_conv_model = TFConv()
  
      # get the result from Tensorflow
      tf_input = tf.convert_to_tensor(imgs)
      tf_x = tf_conv_model(tf_input)
      tf_result = tf_x.numpy()
  
      print(tf_result)
  
  
  if __name__ == '__main__':
      # unittest.main()
      other()