code: def partially_apply_saved_transform_internal(
      saved_model_dir, logical_input_map, tensor_replacement_map=None
  ):
      """Apply a transform graph, represented as a SavedModel, to existing Tensors.
  
      For internal use only.  Users should use the `transform_raw_features` or
      `transform_raw_features_layer` method of the TFTrandformOutput class.
  
      This adds nodes to a graph that already contains Tensors representing the
      inputs.  These input Tensors may be placeholders that will be fed when the
      graph is executed, or may be the outputs of some Ops.  Most typically, the
      input Tensors are reading and/or parsing Ops, but they could be anything--
      including the outputs of a prior application of this function using another
      transform graph.
  
      This function operates on the default Graph in the default Session, and so
      must be called within a context where these are provided.
  
      Args:
        saved_model_dir: A SavedModel directory providing a transform
          graph.  The MetaGraphDef and signature are selected from the SavedModel
          using keys defined in `../constants.py` ('transform' and
          'transform_signature', respectively).
        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.
        tensor_replacement_map: a dict of tensor names to `Tensors`.
  
      Returns:
        A pair of (unbound_inputs, outputs) where unbound_inputs is a dict of
        logical name to Tensors that are yet to be mapped or fed, and outputs is
        a dict of logical name to Tensor, as provided by the output signature
        of the transform graph
  
      Raises:
        ValueError: if the provided input_tensors dict has keys that are not part
          of the input signature, or any of the provided inputs have the wrong
          type or shape.
        RuntimeError: if there is no default graph available to which to apply the
          transform.
      """
      unbound_inputs, outputs = _partially_apply_saved_transform_impl(
          saved_model_dir, logical_input_map, tensor_replacement_map
      )
      return unbound_inputs, outputs
  
file_path: transform/tensorflow_transform/saved/saved_transform_io.py
imports: import importlib
  import os
  import re
  import tensorflow
  from tensorflow_transform.py_func import pyfunc_helper
  from tensorflow_transform.saved import constants
  from tensorflow_transform.saved import saved_model_loader
  from tensorflow.core.protobuf import struct_pb2
  from tensorflow.python.framework import ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.training import saver
label_desc: Use image node_id 6 for calling a global function with example usage: partially_apply_saved_transform_internal(saved_model_dir, logical_input_map, tensor_replacement_map) and returns: unbound_inputs,  outputs