class_docstr: A wrapper around the output of the tf.Transform.
docstr: Returns a feature_spec for the transformed features.
  
  Returns:
    A dict from feature names to FixedLenFeature/SparseFeature/VarLenFeature.
docstr_tok: ["Returns","a","feature_spec","for","the","transformed","features",".","Returns",":","A","dict","from","feature","names","to","FixedLenFeature\/SparseFeature\/VarLenFeature","."]
code: def transformed_feature_spec(
      self,
  ) -> Dict[str, common_types.FeatureSpecType]:
      """Returns a feature_spec for the transformed features.
  
      Returns:
        A dict from feature names to FixedLenFeature/SparseFeature/VarLenFeature.
      """
      return schema_utils.schema_as_feature_spec(
          self.transformed_metadata.schema
      ).feature_spec
  
imports: import json
  import os
  from typing import Any, Dict, List, Mapping, Optional
  import numpy
  import tensorflow
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_tools
  from tensorflow_transform.analyzers import sanitized_vocab_filename
  from tensorflow_transform.keras_lib import tf_keras
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow_transform.saved import saved_transform_io_v2
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tensorflow_transform.tf_metadata import metadata_io
  from tensorflow_transform.tf_metadata import schema_utils
  from tensorflow.python.framework import ops
  from tensorflow.tools.docs import doc_controls
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 6 for calling the TFTransformOutput obj's underlying member method code with example usage: obj.transformed_feature_spec() and returns: schema_utils