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: Estimated stepping batches for the complete training inferred from DataLoaders, gradient accumulation factor
  and distributed setup.
  
  Examples
  ________
  
  .. code-block:: python
  
      def configure_optimizers(self):
          optimizer = ...
          scheduler = torch.optim.lr_scheduler.OneCycleLR(
              optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
          )
          return [optimizer], [scheduler]
docstr_tok: ["Estimated","stepping","batches","for","the","complete","training","inferred","from","DataLoaders",",","gradient","accumulation","factor","and","distributed","setup",".","Examples","________","..","code-block",":",":","python","def","configure_optimizers","(","self",")",":","optimizer","=","...","scheduler","=","torch.optim.lr_scheduler.OneCycleLR","(","optimizer",",","max_lr=1e-3",",","total_steps=self.trainer.estimated_stepping_batches",")","return","[","optimizer","]",",","[","scheduler","]"]
code: def estimated_stepping_batches(self) -> Union[int, float]:
      """Estimated stepping batches for the complete training inferred from DataLoaders, gradient accumulation factor
      and distributed setup.
  
      Examples
      ________
  
      .. code-block:: python
  
          def configure_optimizers(self):
              optimizer = ...
              scheduler = torch.optim.lr_scheduler.OneCycleLR(
                  optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
              )
              return [optimizer], [scheduler]
  
      """
      return super().estimated_stepping_batches
  
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 9 for calling the Trainer obj's underlying member method code with example usage: obj.estimated_stepping_batches() and returns: super