class_docstr: Implementation of a configurable command line tool for pytorch-lightning.
class_docstr_tok: ["Implementation","of","a","configurable","command","line","tool","for","pytorch-lightning","."]
docstr: Instantiates the trainer.
  
  Args:
      kwargs: Any custom trainer arguments.
code: def instantiate_trainer(self, **kwargs: Any) -> Trainer:
      """Instantiates the trainer.
  
      Args:
          kwargs: Any custom trainer arguments.
  
      """
      extra_callbacks = [
          self._get(self.config_init, c)
          for c in self._parser(self.subcommand).callback_keys
      ]
      trainer_config = {
          **self._get(self.config_init, "trainer", default={}),
          **kwargs,
      }
      return self._instantiate_trainer(trainer_config, extra_callbacks)
  
imports: import os
  import sys
  from functools import partial, update_wrapper
  from types import MethodType
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from lightning_utilities.core.rank_zero import _warn
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.cloud_io import get_filesystem
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch import Callback, LightningDataModule, LightningModule, Trainer, seed_everything
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
label_desc: Use image node_id 16 for calling the LightningCLI obj's underlying member method code with example usage: obj.instantiate_trainer() and returns: self