def _build(self):
    """Builds block according to the arguments."""
    filters = self._block_args.input_filters * self._block_args.expand_ratio
    if self._block_args.expand_ratio != 1:
      # Expansion phase:
      self._expand_conv = tf.layers.Conv2D(
          filters,
          kernel_size=[3, 3],
          strides=[1, 1],
          kernel_initializer=conv_kernel_initializer,
          padding='same',
          use_bias=False)
      self._bn0 = self._batch_norm(
          axis=self._channel_axis,
          momentum=self._batch_norm_momentum,
          epsilon=self._batch_norm_epsilon)

    # Output phase:
    filters = self._block_args.output_filters
    self._project_conv = tf.layers.Conv2D(
        filters,
        kernel_size=[1, 1],
        strides=self._block_args.strides,
        kernel_initializer=conv_kernel_initializer,
        padding='same',
        use_bias=False)
    self._bn1 = self._batch_norm(
        axis=self._channel_axis,
        momentum=self._batch_norm_momentum,
        epsilon=self._batch_norm_epsilon)
Builds block according to the arguments.
tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_model.py