class_docstr: Clip tensor
  
  Args:
      clip_value_min: A number or tensor of lower bound to clip
      clip_value_max: A number of tensor of upper bound to clip
      learn_min: A boolean. If True, learn min. clip_value_min will be used to initialize. Default False
      learn_max: A boolean. Similar as learn_min but for max.
  
  Raises:
      ValueError:
code: def forward(self, inputs):
      self.clip_value_min = -self.clip_value_max
      outputs = QF.clip(
          inputs, self.clip_value_min, self.clip_value_max
      )
      return outputs
  
file_path: lightseq/lightseq/training/pytorch_quantization/nn/modules/clip.py
imports: import torch
  from torch import nn
  from torch.nn.parameter import Parameter
  from lightseq.training.pytorch_quantization.nn import functional
label_desc: Use image node_id 2 for calling the Clip obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs