def _apply_v2_transform_model_finalized(
      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`. It assumes that self.finalize has been called before
    this method is invoked.

    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.
    """

    # Assert that the same keys are fed as this model was finalized with.
    unfed_input_keys = self._get_unfed_input_keys(logical_input_map.keys())
    assert sorted(unfed_input_keys) == self._sorted_unfed_input_keys

    modified_inputs = self._format_input_map_as_tensors(logical_input_map)
    return self._wrapped_function_finalized(modified_inputs)
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`. It assumes that self.finalize has been called before
    this method is invoked.

    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.
    
tensorflow_transform/saved/saved_transform_io_v2.py