code: def __init__(
      self,
      backbone: nn.Module,
      head: str,
      head_config: Dict,
      config: DictConfig,
      custom_loss: Optional[torch.nn.Module] = None,
      custom_metrics: Optional[List[Callable]] = None,
      custom_metrics_prob_inputs: Optional[List[bool]] = None,
      custom_optimizer: Optional[torch.optim.Optimizer] = None,
      custom_optimizer_params: Dict = {},
      **kwargs,
  ):
      assert (
          hasattr(config, "loss") or custom_loss is not None
      ), "Loss function not defined in the config"
      super().__init__(
          config,
          custom_loss,
          custom_metrics,
          custom_metrics_prob_inputs,
          custom_optimizer,
          custom_optimizer_params,
          head=head,
          head_config=head_config,
          backbone=backbone,
          **kwargs,
      )
  
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 1 to create a new _GenericModel object from inherited base classes: BaseModel with example: obj = _GenericModel(backbone, head, head_config, config, custom_loss, custom_metrics, custom_metrics_prob_inputs, custom_optimizer, custom_optimizer_params)