class_docstr: A wrapper around the output of the tf.Transform.
docstr: Takes a dict of tensors representing raw features and transforms them.
  
  Takes a dictionary of `Tensor`, `SparseTensor`, or `RaggedTensor`s that
  represent the raw features, and applies the transformation defined by
  tf.Transform.
  
  If False it returns all transformed features defined by tf.Transform. To
  only return features transformed from the given 'raw_features', set
  `drop_unused_features` to True.
  
  Note: If eager execution is enabled and this API is invoked inside a
  tf.function or an API that uses tf.function such as dataset.map, please use
  `transform_features_layer` instead. It separates out loading of the
  transform graph and hence resources will not be initialized on each
  invocation. This can have significant performance improvement if the
  transform graph was exported as a TF1 SavedModel and guarantees correctness
  if it was exported as a TF2 SavedModel.
  
  Args:
    raw_features: A dict whose keys are feature names and values are
    `Tensor`s, `SparseTensor`s, or `RaggedTensor`s.
    drop_unused_features: If True, the result will be filtered. Only the
      features that are transformed from 'raw_features' will be included in
      the returned result. If a feature is transformed from multiple raw
      features (e.g, feature cross), it will only be included if all its base
      raw features are present in `raw_features`.
  
  Returns:
    A dict whose keys are feature names and values are `Tensor`s,
    `SparseTensor`s, or `RaggedTensor`s representing transformed features.
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code: def transform_raw_features(
      self,
      raw_features: Mapping[str, common_types.TensorType],
      drop_unused_features: bool = True,  # LEGACY_VALUE=False
  ) -> Dict[str, common_types.TensorType]:
      """Takes a dict of tensors representing raw features and transforms them.
  
      Takes a dictionary of `Tensor`, `SparseTensor`, or `RaggedTensor`s that
      represent the raw features, and applies the transformation defined by
      tf.Transform.
  
      If False it returns all transformed features defined by tf.Transform. To
      only return features transformed from the given 'raw_features', set
      `drop_unused_features` to True.
  
      Note: If eager execution is enabled and this API is invoked inside a
      tf.function or an API that uses tf.function such as dataset.map, please use
      `transform_features_layer` instead. It separates out loading of the
      transform graph and hence resources will not be initialized on each
      invocation. This can have significant performance improvement if the
      transform graph was exported as a TF1 SavedModel and guarantees correctness
      if it was exported as a TF2 SavedModel.
  
      Args:
        raw_features: A dict whose keys are feature names and values are
        `Tensor`s, `SparseTensor`s, or `RaggedTensor`s.
        drop_unused_features: If True, the result will be filtered. Only the
          features that are transformed from 'raw_features' will be included in
          the returned result. If a feature is transformed from multiple raw
          features (e.g, feature cross), it will only be included if all its base
          raw features are present in `raw_features`.
  
      Returns:
        A dict whose keys are feature names and values are `Tensor`s,
        `SparseTensor`s, or `RaggedTensor`s representing transformed features.
      """
      if self._exported_as_v1:
          transformed_features = self._transform_raw_features_compat_v1(
              raw_features, drop_unused_features
          )
      else:
          tft_layer = self.transform_features_layer()
          if not drop_unused_features:
              tf.compat.v1.logging.warning(
                  "Unused features are always dropped in the TF 2.x "
                  "implementation. Ignoring value of drop_unused_features."
              )
  
          transformed_features = tft_layer(raw_features)
      return _TransformedFeaturesDict(transformed_features)
  
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 14 for calling the TFTransformOutput obj's underlying member method code with example usage: obj.transform_raw_features(raw_features, drop_unused_features) and returns: _TransformedFeaturesDict