class_docstr: Exteded Trainer for FLash tasks.
  
  >>> Trainer()  # doctest: +ELLIPSIS
  <...trainer.Trainer object at ...>
class_docstr_tok: ["Exteded","Trainer","for","FLash","tasks",".",">",">",">","Trainer","(",")","#","doctest",":","+ELLIPSIS","<","...","trainer.Trainer","object","at","...",">"]
docstr: Runs the full optimization routine. Same as :meth:`pytorch_lightning.Trainer.fit`
  
  Args:
      datamodule: A instance of :class:`LightningDataModule`.
  
      model: Model to fit.
  
      train_dataloader: A Pytorch DataLoader with training samples. If the model has
          a predefined train_dataloader method this will be skipped.
  
      val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples.
          If the model has a predefined val_dataloaders method this will be skipped
docstr_tok: ["Runs","the","full","optimization","routine",".","Same","as",":","meth",":","`","pytorch_lightning.Trainer.fit","`","Args",":","datamodule",":","A","instance","of",":","class",":","`","LightningDataModule","`",".","model",":","Model","to","fit",".","train_dataloader",":","A","Pytorch","DataLoader","with","training","samples",".","If","the","model","has","a","predefined","train_dataloader","method","this","will","be","skipped",".","val_dataloaders",":","Either","a","single","Pytorch","Dataloader","or","a","list","of","them",",","specifying","validation","samples",".","If","the","model","has","a","predefined","val_dataloaders","method","this","will","be","skipped"]
code: def fit(
      self,
      model: LightningModule,
      train_dataloader: Optional[DataLoader] = None,
      val_dataloaders: Optional[
          Union[DataLoader, List[DataLoader]]
      ] = None,
      datamodule: Optional[LightningDataModule] = None,
  ):
      r"""Runs the full optimization routine. Same as :meth:`pytorch_lightning.Trainer.fit`
  
      Args:
          datamodule: A instance of :class:`LightningDataModule`.
  
          model: Model to fit.
  
          train_dataloader: A Pytorch DataLoader with training samples. If the model has
              a predefined train_dataloader method this will be skipped.
  
          val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples.
              If the model has a predefined val_dataloaders method this will be skipped
  
      """
      if any(isinstance(c, BaseFinetuning) for c in self.callbacks):
          # TODO: if we find a finetuning callback in the trainer should we remove it? or just warn the user?
          warnings.warn(
              "Warning: You are calling fit(), but your trainer is using a fine-tuning callback"
          )
      return super().fit(
          model, train_dataloader, val_dataloaders, datamodule
      )
  
imports: import inspect
  import warnings
  from argparse import ArgumentParser, Namespace
  from functools import wraps
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  from pytorch_lightning import LightningDataModule, LightningModule
  from pytorch_lightning import Trainer
  from pytorch_lightning.accelerators.tpu import TPUAccelerator
  from pytorch_lightning.callbacks import BaseFinetuning
  from pytorch_lightning.utilities.argparse import add_argparse_args, get_init_arguments_and_types, parse_env_variables
  from torch.utils.data import DataLoader
  import flash
  from flash.core.data.io.output import Output
  from flash.core.data.io.output_transform import OutputTransform
  from flash.core.data.io.transform_predictions import TransformPredictions
  from flash.core.model import Task
  from flash.core.registry import FlashRegistry
label_desc: Use image node_id 2 for calling the Trainer obj's underlying member method code with example usage: obj.fit(model, train_dataloader, val_dataloaders, datamodule) and returns: super