code: def get_node_map_in_graph(
      meta_graph_def: meta_graph_pb2.MetaGraphDef,
      prefix: str,
      node_suffixes: List[str],
      graph: tf.Graph,
  ) -> Dict[types.FPLKeyType, Dict[str, types.TensorType]]:
      """Like get_node_map, but looks up the nodes in the given graph.
  
      Args:
         meta_graph_def: MetaGraphDef containing the CollectionDefs to extract the
           structure from.
         prefix: Prefix for the CollectionDef names.
         node_suffixes: The suffixes to the prefix to form the names of the
           CollectionDefs to extract the nodes from, e.g. in the example described
           above, node_suffixes would be ['suffix_a', 'suffix_b'].
         graph: TensorFlow graph to lookup the nodes in.
  
      Returns:
        A dictionary of dictionaries like get_node_map, except the values are
        the actual nodes in the graph.
      """
      node_map = get_node_map(meta_graph_def, prefix, node_suffixes)
      result = {}
      for key, elems in node_map.items():
          result[key] = {
              k: encoding.decode_tensor_node(graph, n)
              for k, n in elems.items()
          }
      return result
  
imports: import collections
  from typing import Dict, List, Optional, Tuple, Union
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import constants
  from tensorflow_model_analysis.eval_saved_model import encoding
  from tensorflow_model_analysis.eval_saved_model import util
  from google.protobuf import any_pb2
  from tensorflow.core.protobuf import meta_graph_pb2
label_desc: Use image node_id 11 for calling a global function with example usage: get_node_map_in_graph(meta_graph_def, prefix, node_suffixes, graph) and returns: result