class_docstr: Network layers with biases should not be combined with normalization layers,
  as the bias makes normalization ineffective and can lead to unstable training.
  This verification detects such combinations.
class_docstr_tok: ["Network","layers","with","biases","should","not","be","combined","with","normalization","layers",",","as","the","bias","makes","normalization","ineffective","and","can","lead","to","unstable","training",".","This","verification","detects","such","combinations","."]
code: def _verification_message(self):
      if self._detected_pairs:
          message = "\n\nThe model failed the NormalizationBiasVerification check:"
          for pair in self._detected_pairs:
              message += (
                  f"\n  * Detected a layer '{pair[0]}' with bias followed by"
                  f" a normalization layer '{pair[1]}'."
              )
          message += (
              "\n\nThis makes the normalization ineffective and can lead to unstable training. "
              "Either remove the normalization or turn off the bias.\n\n"
          )
      else:
          message = "\nThe model passed the NormalizationBiasVerification check."
      return message
  
file_path: allennlp/allennlp/confidence_checks/normalization_bias_verification.py
imports: import torch
  from torch import nn
  from typing import Tuple, List, Callable
  from allennlp.confidence_checks.verification_base import VerificationBase
  import logging
label_desc: Use image node_id 5 for calling the NormalizationBiasVerification obj's underlying member method code with example usage: obj._verification_message() and returns: message