code: def _setup_metrics(self):
      if self.custom_metrics is None:
          self.metrics = []
          task_module = torchmetrics.functional
          for metric in self.hparams.metrics:
              try:
                  self.metrics.append(getattr(task_module, metric))
              except AttributeError as e:
                  logger.error(
                      f"{metric} is not a valid functional metric defined in the torchmetrics.functional module"
                  )
                  raise e
      else:
          self.metrics = self.custom_metrics
  
imports: import importlib
  import warnings
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from pandas import DataFrame
  from torch import Tensor
  from torch.optim import Optimizer
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import PreEncoded1dLayer
  from pytorch_tabular.utils import get_logger, reset_all_weights
label_desc: Use image node_id 9 for calling the BaseModel obj's underlying member method code with example usage: obj._setup_metrics() without return types