code: def __init__(
      self,
      model: nn.Module,
      dataset_splits: DatasetSplits,
      loss: nn.Module,
      optimizer: optim.Optimizer,
      metrics: Dict[str, Metric],
      device: str = None,
      num_epochs: int = 1,
      seed: int = None,
      cuda: bool = None,
      loss_accumulation_steps: int = 4,
      scheduler: Any = None,
      regularizer: RegularizerABC = None,
      gradient_clipping: float = 1.0,
      output_transform=None,
      tensorboard_logs: str = None,
  ):
      self.model: nn.Module = model
  
      self.forward_param_defaults = {}
  
      model_spec = inspect.getfullargspec(model.forward)
      self.forward_params: List[str] = model_spec.args[1:]
      for fparam, pdefault in zip(
          reversed(model_spec.args[1:]),
          reversed(model_spec.defaults if model_spec.defaults else []),
      ):
          self.forward_param_defaults[fparam] = pdefault
  
      self.dataset_splits: DatasetSplits = dataset_splits
      self.loss: nn.Module = loss
      self.optimizer: optim.Optimizer = optimizer
      self.metrics: Dict[str, Metric] = metrics
      self.metrics: List[Metric] = [
          metric for _, metric in self.metrics.items()
      ]
      self.device: str = device
      self.num_epochs: int = num_epochs
      self.scheduler: Any = scheduler
      self.seed: int = seed
      self.cuda: bool = cuda
      if (
          self.cuda is None
      ):  # If cuda not specified, just check if the cuda is available and use accordingly
          self.device = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
      self.loss_accumulation_steps: int = loss_accumulation_steps
      self.regularizer: RegularizerABC = regularizer
      self.gradient_clipping: float = gradient_clipping
      self.output_transform = output_transform
      self.tensorboard_logs: str = tensorboard_logs
      if self.tensorboard_logs and TENSORBOARD:
          self.writer = SummaryWriter(log_dir=self.tensorboard_logs)
  
      if not self.output_transform:
          self.output_transform = lambda y_pred, y_target, loss: (
              y_pred,
              y_target,
              loss,
          )
  
      self.eval_output_transform = lambda y, y_pred: (y, y_pred)
  
      (
          self.trainer,
          self.training_metrics,
      ) = self.create_supervised_trainer()
      self.evaluator = self.create_supervised_evaluator()
  
      loss_metrics = [m for m in self.metrics if isinstance(m, Loss)]
  
      if self.scheduler:
          if not loss_metrics:
              raise ValueError("A loss metric must be configured")
          elif len(loss_metrics) > 1:
              logging.warning(
                  "multiple loss metrics detected, using %s for LR scheduling",
                  loss_metrics[0],
              )
          self.loss_metric = loss_metrics[0]
  
      self.metrics_history = {
          "training": defaultdict(list),
          "validation": defaultdict(list),
          "test": defaultdict(list),
      }
  
      self.setup(self.training_metrics)
  
imports: import inspect
  import logging
  import re
  from abc import abstractmethod
  from collections import defaultdict
  from typing import Dict, List, Any, Union, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from ignite.contrib.handlers.tensorboard_logger import TensorboardLogger, OutputHandler, OptimizerParamsHandler, WeightsScalarHandler, WeightsHistHandler, GradsScalarHandler
  from ignite.contrib.handlers.tqdm_logger import ProgressBar
  from ignite.engine import Events
  from ignite.engine.engine import Engine
  from ignite.metrics import Loss, Metric, RunningAverage, MetricsLambda, Accuracy
  from ignite.utils import convert_tensor
  from transfer_nlp.loaders.loaders import DatasetSplits
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.regularizers import RegularizerABC
  from transfer_nlp.plugins.trainer_abc import TrainerABC
label_desc: Use image node_id 1 to create a new BaseIgniteTrainer object from inherited base classes: TrainerABC with example: obj = BaseIgniteTrainer(model, dataset_splits, loss, optimizer, metrics, device, num_epochs, seed, cuda, loss_accumulation_steps, scheduler, regularizer, gradient_clipping, output_transform, tensorboard_logs)