code: def load_and_resolve_feature_metadata(
      eval_saved_model_path: str, graph: tf.Graph
  ):
      """Get feature data (feature columns, feature) from EvalSavedModel metadata.
  
      Like load_feature_metadata, but additionally resolves the Tensors in the given
      graph.
  
      Args:
        eval_saved_model_path: Path to EvalSavedModel, for the purposes of loading
          the feature_metadata file.
        graph: tf.Graph to resolve the Tensors in.
  
      Returns:
        Same as load_feature_metadata, except associated_tensors and features
        contain the Tensors resolved in the graph instead of TensorInfos.
      """
      result = load_feature_metadata(
          eval_saved_model_path=eval_saved_model_path
      )
  
      # Resolve Tensors in graph
      result["associated_tensors"] = [
          tf.compat.v1.saved_model.get_tensor_from_tensor_info(
              tensor_info, graph
          )
          for tensor_info in result["associated_tensors"]
      ]
      result["features"] = {
          k: tf.compat.v1.saved_model.get_tensor_from_tensor_info(
              v, graph
          )
          for k, v in result[
              "features"
          ].items()  # pytype: disable=attribute-error  # always-use-return-annotations
      }
  
      return result
  
imports: import contextlib
  import inspect
  import os
  import pickle
  from typing import Any, Callable, Dict, Optional
  import six
  import tensorflow
  from tensorflow import estimator
  import tensorflow_model_analysis
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.utils import util
  from tensorflow.python.feature_column import feature_column_v2
  from tensorflow_estimator.python.estimator.canned import dnn
label_desc: Use image node_id 12 for calling a global function with example usage: load_and_resolve_feature_metadata(eval_saved_model_path, graph) and returns: result