code: def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
      '''
      convolutions-only net
  
      Parameters:
      ----------
  
      conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
  
      Returns:
  
      function that takes tensorflow tensor as input and returns the output of the last convolutional layer
  
      '''
  
      def network_fn(X):
          out = tf.cast(X, tf.float32) / 255.
          with tf.variable_scope("convnet"):
              for num_outputs, kernel_size, stride in convs:
                  out = layers.convolution2d(out,
                                             num_outputs=num_outputs,
                                             kernel_size=kernel_size,
                                             stride=stride,
                                             activation_fn=tf.nn.relu,
                                             **conv_kwargs)
  
          return out
      return network_ff conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
      '''
      convolutions-only net
  
      Parameters:
      ----------
  
      conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
  
      Returns:
  
      function that takes tensorflow tensor as input and returns the output of the last convolutional layer
  
      '''
  
      def network_fn(X):
          out = tf.cast(X, tf.float32) / 255.
          with tf.variable_scope("convnet"):
              for num_outputs, kernel_size, stride in convs:
                  out = layers.convolution2d(out,
                                             num_outputs=num_outputs,
                                             kernel_size=kernel_size,
                                             stride=stride,
                                             activation_fn=tf.nn.relu,
                                             **conv_kwargs)
  
          return out
      return network_fn
docs: convolutions-only net
  
      Parameters:
      ----------
  
      conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
  
      Returns:
  
      function that takes tensorflow tensor as input and returns the output of the last convolutional layer