class_docstr: A standard interface for optimizers wrapped by the Booster.
  
  Args:
      optim (Optimizer): The optimizer to be wrapped.
docstr: Clips gradient of an iterable of parameters at specified min and max values.
  
  Args:
      clip_value (float or int): maximum allowed value of the gradients. Gradients are clipped in the range
  
  Note:
      In PyTorch Torch 2.0 and above, you can pass in foreach=True as kwargs to clip_grad_value_ to use the
      faster implementation. Please refer to the PyTorch documentation for more details.
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code: def clip_grad_by_value(
      self, clip_value: float, *args, **kwargs
  ) -> None:
      """
      Clips gradient of an iterable of parameters at specified min and max values.
  
      Args:
          clip_value (float or int): maximum allowed value of the gradients. Gradients are clipped in the range
  
      Note:
          In PyTorch Torch 2.0 and above, you can pass in foreach=True as kwargs to clip_grad_value_ to use the
          faster implementation. Please refer to the PyTorch documentation for more details.
      """
      nn.utils.clip_grad_value_(
          self.parameters, clip_value, *args, **kwargs
      )
  
imports: from typing import Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.optim import Optimizer
label_desc: Use image node_id 12 for calling the OptimizerWrapper obj's underlying member method code with example usage: obj.clip_grad_by_value(clip_value) without return types