class_docstr: Handles a SavedModel exported using TF 1.x APIs in TF 2.x.
class_docstr_tok: ["Handles","a","SavedModel","exported","using","TF","1.x","APIs","in","TF","2.x","."]
docstr: Applies a V2 transform graph to dictionary of (Composite)Tensors.
  
  This method applies the transformation graph to the `logical_input_map` to
  return only outputs that can be computed from the keys provided in
  `logical_input_map`.
  
  Args:
    logical_input_map: a dict of logical name to Tensor.  The logical names
      must be a subset of those in the input signature of the transform graph,
      and the corresponding Tensors must have the expected types and shapes.
  
  Returns:
    A dict of logical name to Tensor, as provided by the output signature of
    the transform graph.
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code: def _apply_v2_transform_model(
      self, logical_input_map: Mapping[str, common_types.TensorType]
  ) -> Dict[str, common_types.TensorType]:
      """Applies a V2 transform graph to dictionary of (Composite)Tensors.
  
      This method applies the transformation graph to the `logical_input_map` to
      return only outputs that can be computed from the keys provided in
      `logical_input_map`.
  
      Args:
        logical_input_map: a dict of logical name to Tensor.  The logical names
          must be a subset of those in the input signature of the transform graph,
          and the corresponding Tensors must have the expected types and shapes.
  
      Returns:
        A dict of logical name to Tensor, as provided by the output signature of
        the transform graph.
      """
  
      unfed_input_keys = self._get_unfed_input_keys(
          logical_input_map.keys()
      )
      feeds = self._get_feeds(unfed_input_keys)
      modified_inputs = self._format_input_map_as_tensors(
          logical_input_map
      )
  
      if unfed_input_keys:
          batch_size = 1
          if logical_input_map:
              an_input = next(iter(logical_input_map.values()))
              if isinstance(an_input, tf.RaggedTensor):
                  batch_size = an_input.bounding_shape(axis=0)
              elif tf.shape(an_input)[0] is not None:
                  batch_size = tf.shape(an_input)[0]
  
          missing_inputs = self._get_missing_inputs(
              unfed_input_keys, batch_size
          )
          modified_inputs.update(missing_inputs)
  
      flattened_inputs = tf.nest.flatten(
          modified_inputs, expand_composites=True
      )
  
      # self._wrapped_function.inputs may be longer than flattened_inputs as it
      # also contains captured inputs. However, we only want the user inputs here
      # so we don't assert equal length.
      for input_t, wrapped_input in zip(
          flattened_inputs, self._wrapped_function.inputs
      ):
          try:
              wrapped_input.shape.assert_is_compatible_with(
                  input_t.shape
              )
          except ValueError as e:
              raise ValueError("{}: {}".format(input_t, e))
  
      transformed_features = self._wrapped_function(*flattened_inputs)
      fetches_keys = self._get_fetches_keys(feeds)
      return {key: transformed_features[key] for key in fetches_keys}
  
imports: from typing import Any, Dict, Iterable, Mapping, Tuple, Union
  import tensorflow
  from tensorflow_transform import annotators
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_tools
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.py_func import pyfunc_helper
  from tensorflow_transform.saved import constants
  from tensorflow_transform.saved import saved_model_loader
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow.python.eager import function
  from tensorflow.python.eager import wrap_function
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.util import object_identity
label_desc: Use image node_id 13 for calling the SavedModelLoader obj's underlying member method code with example usage: obj._apply_v2_transform_model(logical_input_map) and returns: unknown