class_docstr: Performs automatic optimization (forward, zero grad, backward, optimizer step)
docstr: Build the step function that runs the `training_step` and processes its output.
code: def _make_step_fn(
      self, kwargs: OrderedDict
  ) -> Callable[[], ClosureResult]:
      """Build the step function that runs the `training_step` and processes its output."""
      return partial(self._training_step, kwargs)
  
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 4 for calling the _AutomaticOptimization obj's underlying member method code with example usage: obj._make_step_fn(kwargs) and returns: partial