code: def clip_gradients_norm(
      self,
      module: "DeepSpeedEngine",
      optimizer: Optimizer,
      max_norm: Union[float, int],
      norm_type: Union[float, int] = 2.0,
      error_if_nonfinite: bool = True,
  ) -> torch.Tensor:
      raise NotImplementedError(
          "DeepSpeed handles gradient clipping automatically within the optimizer. "
          "Make sure to set the `gradient_clipping` value in your Config."
      )
  
imports: import argparse
  import json
  import logging
  import os
  import platform
  from contextlib import ExitStack
  from itertools import chain
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Callable, ContextManager, Dict, List, Mapping, Optional, Tuple, Union
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from torch.nn import Module
  from torch.optim import Optimizer
  from typing_extensions import override
  from lightning.fabric.accelerators import Accelerator, CUDAAccelerator
  from lightning.fabric.plugins.environments.cluster_environment import ClusterEnvironment
  from lightning.fabric.plugins.precision import Precision
  from lightning.fabric.strategies.ddp import DDPStrategy
  from lightning.fabric.strategies.registry import _StrategyRegistry
  from lightning.fabric.strategies.strategy import _Sharded
  from lightning.fabric.utilities.distributed import log
  from lightning.fabric.utilities.load import _move_state_into
  from lightning.fabric.utilities.rank_zero import rank_zero_info, rank_zero_warn
  from lightning.fabric.utilities.seed import reset_seed
  from lightning.fabric.utilities.types import _PATH
label_desc: Use image node_id 12 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj.clip_gradients_norm(module, optimizer, max_norm, norm_type, error_if_nonfinite) without return types