class_docstr: Performs automatic optimization (forward, zero grad, backward, optimizer step)
docstr: Build a closure object that captures the given arguments and runs the `training_step` function and
  optionally other functions such as `backward` and `zero_grad`.
code: def _make_closure(
      self, kwargs: OrderedDict, optimizer: Optimizer, batch_idx: int
  ) -> Closure:
      """Build a closure object that captures the given arguments and runs the `training_step` function and
      optionally other functions such as `backward` and `zero_grad`."""
      step_fn = self._make_step_fn(kwargs)
      backward_fn = self._make_backward_fn(optimizer)
      zero_grad_fn = self._make_zero_grad_fn(batch_idx, optimizer)
      return Closure(
          step_fn=step_fn,
          backward_fn=backward_fn,
          zero_grad_fn=zero_grad_fn,
      )
  
imports: from dataclasses import dataclass, field
  from functools import partial
  from typing import Any, Callable, Dict, Mapping, Optional, OrderedDict
  import torch
  from torch import Tensor
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.pytorch.loops.loop import _Loop
  from lightning.pytorch.loops.optimization.closure import AbstractClosure, OutputResult
  from lightning.pytorch.loops.progress import _OptimizationProgress
  from lightning.pytorch.loops.utilities import _block_parallel_sync_behavior
  from lightning.pytorch.trainer import call
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.rank_zero import WarningCache
  from lightning.pytorch.utilities.types import STEP_OUTPUT
label_desc: Use image node_id 3 for calling the _AutomaticOptimization obj's underlying member method code with example usage: obj._make_closure(kwargs, optimizer, batch_idx) and returns: Closure