code: def get_approx_errors(expected_tensors, observed_tensors):
      """
      Calculating relative error for multiple tensors: Dict[tensors_name: str, tensor: np.ndarray]
      """
      errors = {}
      for node_name in observed_tensors.keys():
          expected_tensor = expected_tensors[node_name][node_name]
          observed_tensor = observed_tensors[node_name][node_name]
          errors[node_name] = calc_approx_error(
              expected_tensor, observed_tensor
          )
      return errors
  
file_path: mxnet/example/quantization_inc/custom_strategy.py
imports: import copy
  import numpy
  from collections import OrderedDict
  from neural_compressor.strategy.strategy import TuneStrategy, strategy_registry
label_desc: Use image node_id 2 for calling a global function with example usage: get_approx_errors(expected_tensors, observed_tensors) and returns: errors