code: def neural_gpu_body(inputs, hparams, name=None):
      """The core Neural GPU."""
      with tf.variable_scope(name, "neural_gpu"):
  
          def step(state, inp):  # pylint: disable=missing-docstring
              x = tf.nn.dropout(state, 1.0 - hparams.dropout)
              for layer in range(hparams.num_hidden_layers):
                  x = common_layers.conv_gru(
                      x,
                      (hparams.kernel_height, hparams.kernel_width),
                      hparams.hidden_size,
                      name="cgru_%d" % layer,
                  )
              # Padding input is zeroed-out in the modality, we check this by summing.
              padding_inp = tf.less(
                  tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001
              )
              new_state = tf.where(
                  padding_inp, state, x
              )  # No-op where inp is padding.
              return new_state
  
          return tf.foldl(
              step,
              tf.transpose(inputs, [1, 0, 2, 3]),
              initializer=inputs,
              parallel_iterations=1,
              swap_memory=True,
          )
  
file_path: tensor2tensor/tensor2tensor/models/neural_gpu.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: neural_gpu_body(inputs, hparams, name) and returns: tf, new_state