class_docstr: Visitor that translates the operation graph.
  
  The original graph is defined by the user in the preprocessing_fn.  The
  translated graph represents a Beam pipeline.
code: def visit(self, operation_def, input_values):
      if isinstance(operation_def, analyzer_nodes.TensorSource):
          tensors = operation_def.tensors
          label = operation_def.label
          # Add tensor to signature so it gets produced by the SavedModel.
          for tensor in tensors:
              self.intermediate_output_signature[
                  _tensor_name(tensor)
              ] = tensor
          keys = tuple(map(_tensor_name, tensors))
          output = nodes.apply_operation(
              beam_nodes.ExtractFromDict,
              self.extracted_values_dict,
              keys=keys,
              label=label,
          )
          return (output,)
      else:
          return nodes.OperationNode(
              operation_def, input_values
          ).outputs
  
imports: import collections
  import dataclasses
  import hashlib
  from typing import Collection, Dict, Mapping, Optional, Tuple
  from typing import OrderedDict
  import tensorflow
  from tensorflow_transform import analyzer_nodes
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_tools
  from tensorflow_transform import impl_helper
  from tensorflow_transform import nodes
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.beam import analyzer_cache
  from tensorflow_transform.beam import beam_nodes
  from tensorflow_transform.beam import combiner_packing_util
label_desc: Use image node_id 2 for calling the _TranslateVisitor obj's underlying member method code with example usage: obj.visit(operation_def, input_values) and returns: output, nodes