class_docstr: A Keras layer for applying a tf.Transform output to input layers.
code: def call(  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
      self, inputs: Mapping[str, common_types.TensorType]
  ) -> Dict[str, common_types.TensorType]:
      if (
          self._exported_as_v1
          and not ops.executing_eagerly_outside_functions()
      ):
          tf.compat.v1.logging.warning(
              "Falling back to transform_raw_features..."
          )
          return self._tft_output._transform_raw_features_compat_v1(  # pylint: disable=protected-access
              inputs, drop_unused_features=True
          )
      else:
          return self._saved_model_loader.apply_transform_model(inputs)
  
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 4 for calling the TransformFeaturesLayer obj's underlying member method code with example usage: obj.call(inputs) and returns: self, self