code: def apply_vocabulary(
      x: common_types.ConsistentTensorType,
      deferred_vocab_filename_tensor: common_types.TemporaryAnalyzerOutputType,
      *,  # Force passing optional parameters by keys.
      default_value: Any = -1,
      num_oov_buckets: int = 0,
      lookup_fn: Optional[
          Callable[
              [common_types.TensorType, tf.Tensor],
              Tuple[tf.Tensor, tf.Tensor],
          ]
      ] = None,
      file_format: common_types.VocabularyFileFormatType = analyzers.DEFAULT_VOCABULARY_FILE_FORMAT,
      name: Optional[str] = None,
  ) -> common_types.ConsistentTensorType:
      r"""Maps `x` to a vocabulary specified by the deferred tensor.
  
      This function also writes domain statistics about the vocabulary min and max
      values. Note that the min and max are inclusive, and depend on the vocab size,
      num_oov_buckets and default_value.
  
      Args:
        x: A categorical `Tensor`, `SparseTensor`, or `RaggedTensor` of type
          tf.string or tf.int[8|16|32|64] to which the vocabulary transformation
          should be applied. The column names are those intended for the transformed
          tensors.
        deferred_vocab_filename_tensor: The deferred vocab filename tensor as
          returned by `tft.vocabulary`, as long as the frequencies were not stored.
        default_value: The value to use for out-of-vocabulary values, unless
          'num_oov_buckets' is greater than zero.
        num_oov_buckets:  Any lookup of an out-of-vocabulary token will return a
          bucket ID based on its hash if `num_oov_buckets` is greater than zero.
          Otherwise it is assigned the `default_value`.
        lookup_fn: Optional lookup function, if specified it should take a tensor
          and a deferred vocab filename as an input and return a lookup `op` along
          with the table size, by default `apply_vocabulary` constructs a
          StaticHashTable for the table lookup.
        file_format: (Optional) A str. The format of the given vocabulary. Accepted
          formats are: 'tfrecord_gzip', 'text'. The default value is 'text'.
        name: (Optional) A name for this operation.
  
      Returns:
        A `Tensor`, `SparseTensor`, or `RaggedTensor` where each string value is
        mapped to an integer. Each unique string value that appears in the
        vocabulary is mapped to a different integer and integers are consecutive
        starting from zero, and string value not in the vocabulary is
        assigned default_value.
      """
      return _apply_vocabulary_internal(
          x,
          deferred_vocab_filename_tensor,
          default_value,
          num_oov_buckets,
          lookup_fn,
          file_format,
          False,
          name,
      )
  
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 19 for calling a global function with example usage: apply_vocabulary(x, deferred_vocab_filename_tensor) and returns: _apply_vocabulary_internal