code: def supply_missing_tensor(
      batch_size: int,
      tensor_shape: tf.TensorShape,
      tensor_dtype: tf.DType,
  ) -> tf.Tensor:
      """Supplies a `tf.Tensor` compatible with `tensor`.
  
      Supports only string and numeric dtypes.
      Args:
        batch_size: an integer representing the size of the batch returned.
        tensor_shape: a `tf.TensorShape`. The returned tensor will have shape
          compatible with this.
        tensor_dtype: The dtype of the returned tensors.
  
      Returns:
        A batch of `tf.Tensor` tensors.
      """
      # If tensor rank is 0 or unknown, return a scalar.
      if tensor_shape.ndims is None or tensor_shape.ndims == 0:
          return tf.zeros([], dtype=tensor_dtype)
  
      input_shape = tensor_shape.as_list()
      result_shape = [input_shape[0] or batch_size]
  
      for shape in input_shape[1:]:
          if shape is None:
              result_shape = result_shape + [1]
          else:
              result_shape = result_shape + [shape]
      return tf.zeros(result_shape, dtype=tensor_dtype)
  
file_path: transform/tensorflow_transform/tf2_utils.py
imports: import copy
  import itertools
  from typing import Collection, Iterable, Mapping, Optional, Tuple
  import tensorflow
  from tensorflow_transform import common_types
  from tensorflow.python import tf2
  from tensorflow.python.framework.func_graph import FuncGraph
label_desc: Use image node_id 3 for calling a global function with example usage: supply_missing_tensor(batch_size, tensor_shape, tensor_dtype) and returns: tf, tf