code: def get_weight_decay_param_names(model: nn.Module):
      """
      Set the layer normalization parameters and other layers' bias parameters not to use weight decay.
  
      Parameters
      ----------
      model
          A Pytorch model.
  
      Returns
      -------
      A list of parameter names not using weight decay.
      """
      # By default, we should not apply weight decay for all the norm layers
      decay_param_names = get_parameter_names(
          model,
          [
              nn.LayerNorm,
              nn.BatchNorm1d,
              nn.BatchNorm2d,
              nn.BatchNorm3d,
              nn.GroupNorm,
          ],
      )
      decay_param_names = [
          name
          for name in decay_param_names
          if (
              "bias" not in name
              and "cls_token" not in name
              and "categorical_feature_tokenizer" not in name
              and "numerical_feature_tokenizer" not in name
          )
      ]
      return decay_param_names
  
imports: import functools
  import logging
  import re
  import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
  from pytorch_metric_learning import distances, losses, miners
  from torch import nn, optim
  from torch.nn import functional
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from transformers import Adafactor
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerConfig, Mask2FormerLoss
  from transformers.trainer_pt_utils import get_parameter_names
  from ..constants import ACC, ACCURACY, AVERAGE_PRECISION, BER, BINARY, BIT_FIT, COLUMN_FEATURES, CONTRASTIVE_LOSS, COSINE_EMBEDDING_LOSS, COSINE_SIMILARITY, CROSS_ENTROPY, DETECTION_METRICS, DIRECT_LOSS, EM, F1, F1_MACRO, F1_MICRO, F1_WEIGHTED, FEATURES, FEW_SHOT_CLASSIFICATION, FM, HIT_RATE, IA3, IA3_BIAS, IA3_LORA, IA3_LORA_BIAS, IA3_LORA_NORM, IA3_NORM, IOU, LOG_LOSS, LORA, LORA_BIAS, LORA_NORM, MAE, MULTI_NEGATIVES_SOFTMAX_LOSS, MULTICLASS, NER, NER_TOKEN_F1, NORM_FIT, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, OVERALL_ACCURACY, PAIR_MARGIN_MINER, PEARSONR, PEFT_STRATEGIES, QUADRATIC_KAPPA, R2, RECALL, REGRESSION, RMSE, ROC_AUC, ROOT_MEAN_SQUARED_ERROR, SEMANTIC_SEGMENTATION, SM, SPEARMANR
  from .losses import BBCEWithLogitLoss, FocalLoss, MultiNegativesSoftmaxLoss, SoftTargetCrossEntropy, StructureLoss
  from .lr_scheduler import get_cosine_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup
  from .semantic_seg_metrics import COD_METRICS_NAMES, Balanced_Error_Rate, Binary_IoU, Multiclass_IoU
label_desc: Use image node_id 6 for calling a global function with example usage: get_weight_decay_param_names(model) and returns: decay_param_names