code: def hash_strings(
      strings: common_types.ConsistentTensorType,
      hash_buckets: int,
      key: Optional[Iterable[int]] = None,
      name: Optional[str] = None,
  ) -> common_types.ConsistentTensorType:
      """Hash strings into buckets.
  
      Args:
        strings: a `Tensor`, `SparseTensor`, or `RaggedTensor` of dtype `tf.string`.
        hash_buckets: the number of hash buckets.
        key: optional. An array of two Python `uint64`. If passed, output will be a
          deterministic function of `strings` and `key`. Note that hashing will be
          slower if this value is specified.
        name: (Optional) A name for this operation.
  
      Returns:
        A `Tensor`, `SparseTensor`, or `RaggedTensor` of dtype `tf.int64` with the
        same shape as
        the input `strings`.
  
      Raises:
        TypeError: if `strings` is not a `Tensor`, `SparseTensor`, or `RaggedTensor`
        of dtype `tf.string`.
      """
      if (
          not isinstance(
              strings, (tf.Tensor, tf.SparseTensor, tf.RaggedTensor)
          )
          or strings.dtype != tf.string
      ):
          raise TypeError(
              "Input to hash_strings must be a `Tensor`, `SparseTensor`, or "
              f"`RaggedTensor` of dtype string; got {strings.dtype}"
          )
      if isinstance(strings, tf.Tensor):
          if name is None:
              name = "hash_strings"
          if key is None:
              return tf.strings.to_hash_bucket_fast(
                  strings, hash_buckets, name=name
              )
          return tf.strings.to_hash_bucket_strong(
              strings, hash_buckets, key, name=name
          )
      else:
          compose_result_fn = (
              _make_composite_tensor_wrapper_if_composite(strings)
          )
          values = tf_utils.get_values(strings)
          return compose_result_fn(
              hash_strings(values, hash_buckets, key)
          )
  
imports: import os
  from typing import Any, Callable, Iterable, Optional, Tuple, Union
  import tensorflow
  from tensorflow_transform import analyzers
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import gaussianization
  from tensorflow_transform import schema_inference
  from tensorflow_transform import tf_utils
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 29 for calling a global function with example usage: hash_strings(strings, hash_buckets, key, name) and returns: tf, compose_result_fn, tf