code: def _pad_util(input_tensor, full_axis_dim):
      """Pad the `input_tensor`'s first dimension to be `full_axis_dim`."""
      missing_axis_dim = (
          full_axis_dim - array_ops.shape_v2(input_tensor)[0]
      )
      tensor_rank = array_ops.rank(input_tensor)
      paddings_axis = [[0, missing_axis_dim]]
      paddings = array_ops.concat(
          [
              paddings_axis,
              array_ops.zeros(
                  shape=(tensor_rank - 1, 2), dtype=dtypes.int32
              ),
          ],
          axis=0,
      )
      padded_input_tensor = array_ops.pad(input_tensor, paddings)
      return padded_input_tensor
  
imports: import copy
  import threading
  from typing import Callable, List, Optional, Union
  from tensorflow.python.distribute import collective_util
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import backprop_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import collective_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nccl_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import core
label_desc: Use image node_id 11 for calling a global function with example usage: _pad_util(input_tensor, full_axis_dim) and returns: padded_input_tensor