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","...",">"]
code: def __init__(self, *args, **kwargs):
      if flash._IS_TESTING:
          if torch.cuda.is_available():
              kwargs["gpus"] = -1
              kwargs["limit_train_batches"] = 1.0
              kwargs["limit_val_batches"] = 1.0
              kwargs["limit_test_batches"] = 1.0
              kwargs["fast_dev_run"] = False
          else:
              kwargs["fast_dev_run"] = True
              kwargs["gpus"] = None
              kwargs["accelerator"] = None
              kwargs["precision"] = 32
      super().__init__(*args, **kwargs)
  
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 1 to create a new Trainer object from inherited base classes: PlTrainer with example: obj = Trainer()