code: def log_norms(self):
      lines = ["NORMS FOR MODEL PARAMTERS"]
      for name, param in self.named_parameters():
          if param.requires_grad and name.split(".")[0] not in (
              "bert_model",
              "forward_charlm",
              "backward_charlm",
          ):
              lines.append("%s %.6g" % (name, torch.norm(param).item()))
      logger.info("\n".join(lines))
  
imports: import logging
  import math
  import os
  import random
  import re
  from types import SimpleNamespace
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import stanza.models.classifiers.data
  from stanza.models.classifiers.base_classifier import BaseClassifier
  from stanza.models.classifiers.data import SentimentDatum
  from stanza.models.classifiers.utils import ExtraVectors, ModelType, build_output_layers
  from stanza.models.common.bert_embedding import extract_bert_embeddings
  from stanza.models.common.data import get_long_tensor, sort_all
  from stanza.models.common.foundation_cache import load_bert
  from stanza.models.common.vocab import PAD_ID, UNK_ID
label_desc: Use image node_id 5 for calling the CNNClassifier obj's underlying member method code with example usage: obj.log_norms() without return types