code: def get_loss_func(
      problem_type: str,
      mixup_active: Optional[bool] = None,
      loss_func_name: Optional[str] = None,
      config: Optional[DictConfig] = None,
      **kwargs,
  ):
      """
      Choose a suitable Pytorch loss module based on the provided problem type.
  
      Parameters
      ----------
      problem_type
          Type of problem.
      mixup_active
          The activation determining whether to use mixup.
      loss_func_name
          The name of the function the user wants to use.
      config
          The optimization configs containing values such as i.e. optimization.loss_function
          An example purpose of this config here is to pass through the parameters for focal loss, i.e.:
              alpha = optimization.focal_loss.alpha
      Returns
      -------
      A Pytorch loss module.
      """
      if problem_type in [BINARY, MULTICLASS]:
          if mixup_active:
              loss_func = SoftTargetCrossEntropy()
          else:
              if (
                  loss_func_name is not None
                  and loss_func_name.lower() == "focal_loss"
              ):
                  loss_func = FocalLoss(
                      alpha=OmegaConf.select(
                          config, "focal_loss.alpha"
                      ),
                      gamma=OmegaConf.select(
                          config, "focal_loss.gamma", default=2.0
                      ),
                      reduction=OmegaConf.select(
                          config, "focal_loss.reduction", default="mean"
                      ),
                  )
              else:
                  loss_func = nn.CrossEntropyLoss()
      elif problem_type == REGRESSION:
          if loss_func_name is not None:
              if "bcewithlogitsloss" in loss_func_name.lower():
                  loss_func = nn.BCEWithLogitsLoss()
              else:
                  loss_func = nn.MSELoss()
          else:
              loss_func = nn.MSELoss()
      elif problem_type == NER:
          loss_func = nn.CrossEntropyLoss(ignore_index=0)
      elif problem_type in [
          OBJECT_DETECTION,
          OPEN_VOCABULARY_OBJECT_DETECTION,
          FEW_SHOT_CLASSIFICATION,
      ]:
          return None
      elif problem_type == SEMANTIC_SEGMENTATION:
          if "structure_loss" in loss_func_name.lower():
              loss_func = StructureLoss()
          elif "balanced_bce" in loss_func_name.lower():
              loss_func = BBCEWithLogitLoss()
          elif "mask2former_loss" in loss_func_name.lower():
              weight_dict = {
                  "loss_cross_entropy": config.mask2former_loss.loss_cross_entropy_weight,
                  "loss_mask": config.mask2former_loss.loss_mask_weight,
                  "loss_dice": config.mask2former_loss.loss_dice_weight,
              }
              loss_func = Mask2FormerLoss(
                  config=Mask2FormerConfig(
                      num_labels=kwargs["num_classes"]
                  ),
                  weight_dict=weight_dict,
              )
          else:
              loss_func = nn.BCEWithLogitsLoss()
      elif problem_type is None:
          return None
      else:
          raise NotImplementedError
  
      return loss_func
  
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 1 for calling a global function with example usage: get_loss_func(problem_type, mixup_active, loss_func_name, config) and returns: loss_func, None, None