code: def get_optimizer(
      optim_type: str,
      optimizer_grouped_parameters,
      lr: float,
      weight_decay: float,
      eps: Optional[float] = 1e-6,
      betas: Optional[Tuple[float, float]] = (0.9, 0.999),
      momentum: Optional[float] = 0.9,
  ):
      """
      Choose a Pytorch optimizer based on its name.
  
      Parameters
      ----------
      optim_type
          Name of optimizer.
      optimizer_grouped_parameters
          The model parameters to be optimized.
      lr
          Learning rate.
      weight_decay
          Optimizer weight decay.
      eps
          Optimizer eps.
      betas
          Optimizer betas.
      momentum
          Momentum used in the SGD optimizer.
  
      Returns
      -------
      A Pytorch optimizer.
      """
      if optim_type == "adamw":
          optimizer = optim.AdamW(
              optimizer_grouped_parameters,
              lr=lr,
              weight_decay=weight_decay,
              eps=eps,
              betas=betas,
          )
      elif optim_type == "adam":
          optimizer = optim.Adam(
              optimizer_grouped_parameters,
              lr=lr,
              weight_decay=weight_decay,
          )
      elif optim_type == "sgd":
          optimizer = optim.SGD(
              optimizer_grouped_parameters,
              lr=lr,
              weight_decay=weight_decay,
              momentum=momentum,
          )
      elif optim_type == "adafactor":
          optimizer = Adafactor(
              optimizer_grouped_parameters,
              lr=lr,
              weight_decay=weight_decay,
              scale_parameter=True,  # Generally recommended to enable scaling
              relative_step=False,
              warmup_init=False,
          )
      else:
          raise ValueError(f"unknown optimizer: {optim_type}")
  
      return optimizer
  
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 4 for calling a global function with example usage: get_optimizer(optim_type, optimizer_grouped_parameters, lr, weight_decay, eps, betas, momentum) and returns: optimizer