code: def _pad_shallow_tensors(tensors, pad_value):
      """Pads the shorter tensors to be as long as the longest."""
      max_dim = 0
      for tensor in tensors:
          dim = tensor.shape.as_list()[-1]
          if dim > max_dim:
              max_dim = dim
  
      output_tensors = []
      for tensor in tensors:
          dim = tensor.shape.as_list()[-1]
          if tensor.shape.as_list()[-1] < max_dim:
              output_tensors.append(
                  tf.pad(
                      tensor,
                      [[0, 0], [0, 0], [0, max_dim - dim]],
                      constant_values=pad_value,
                  )
              )
          else:
              output_tensors.append(tensor)
      print(output_tensors)
  
      return output_tensors
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.models.neural_architecture_search import nas_layers
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 3 for calling a global function with example usage: _pad_shallow_tensors(tensors, pad_value) and returns: output_tensors