class_docstr: Quantizes the given activation.
  
  Args:
      activation_bit (`int`):
          Bitwidth for the quantized activation.
      act_range_momentum (`float`, *optional*, defaults to `0.95`):
          Momentum for updating the activation quantization range.
      per_channel (`bool`, *optional*, defaults to `False`):
          Whether to or not use channel-wise quantization.
      channel_len (`int`, *optional*):
          Specify the channel length when set the *per_channel* True.
      quant_mode (`bool`, *optional*, defaults to `False`):
          Whether or not the layer is quantized.
class_docstr_tok: ["Quantizes","the","given","activation",".","Args",":","activation_bit","(","`","int","`",")",":","Bitwidth","for","the","quantized","activation",".","act_range_momentum","(","`","float","`",",","*","optional","*",",","defaults","to","`","0.95","`",")",":","Momentum","for","updating","the","activation","quantization","range",".","per_channel","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","to","or","not","use","channel-wise","quantization",".","channel_len","(","`","int","`",",","*","optional","*",")",":","Specify","the","channel","length","when","set","the","*","per_channel","*","True",".","quant_mode","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","or","not","the","layer","is","quantized","."]
code: def __repr__(self):
      return (
          f"{self.__class__.__name__}(activation_bit={self.activation_bit}, "
          f"quant_mode: {self.quant_mode}, Act_min: {self.x_min.item():.2f}, "
          f"Act_max: {self.x_max.item():.2f})"
      )
  
imports: import decimal
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Function
  from ...utils import logging
label_desc: Use image node_id 2 for calling the QuantAct obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str, self, str