def unicode_test(seg_instance, seg_str):
            seg_gen_str = str(seg_instance)

            self.assertEqual(len(seg_gen_str), 240)
            self.assertEqual(len(seg_str), 240)
            self.assertEqual(seg_gen_str, seg_str)
def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
                       data_format, name):
  """A single block for ResNet v2, without a bottleneck.

  Batch normalization then ReLu then convolution as described by:
    Identity Mappings in Deep Residual Networks
    https://arxiv.org/pdf/1603.05027.pdf
    by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or [batch,
      height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the convolutions.
    training: A Boolean for whether the model is in training or inference mode.
      Needed for batch normalization.
    projection_shortcut: The function to use for projection shortcuts (typically
      a 1x1 convolution when downsampling the input).
    strides: The block's stride. If greater than 1, this block will ultimately
      downsample the input.
    data_format: The input format ('channels_last' or 'channels_first').
    name: Block name.

  Returns:
    The output tensor of the block; shape should match inputs.
  """
  shortcut = inputs
  first_name = name + 'first'
  inputs = batch_norm(
      inputs, training, data_format, name=first_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=first_name + 'relu')

  # The projection shortcut should come after the first batch norm and ReLU
  # since it performs a 1x1 convolution.
  if projection_shortcut is not None:
    shortcut = projection_shortcut(inputs, name=first_name + 'proj')

  second_name = name + 'second'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=strides,
      data_format=data_format,
      name=second_name + 'input')

  inputs = batch_norm(
      inputs, training, data_format, name=second_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=second_name + 'relu')

  third_name = name + 'third'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=1,
      data_format=data_format,
      name=third_name + 'input')

  return inputs + shortcut