class_docstr: Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
  it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to
  https://arxiv.org/abs/2004.09602.
  
  Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
  initially created.
  
  For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 +
  torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415
code: def __init__(self, min: float, max: float):
      if min > max:
          raise ValueError(
              f"min should be < max (got min: {min}, max: {max})"
          )
  
      super().__init__()
      self.min = min
      self.max = max
  
imports: import math
  from collections import OrderedDict
  import torch
  from packaging import version
  from torch import Tensor, nn
  from .utils import logging
label_desc: Use image node_id 1 to create a new ClippedGELUActivation object from inherited base classes: nn with example: obj = ClippedGELUActivation(min, max)