code: def __init__(self, in_channels, squeeze, activation, quantized=False):
      super().__init__(in_channels, squeeze, activation)
      self.quantized = quantized
      if quantized:
          assert (
              quant_nn is not None
          ), "pytorch_quantization is not available"
          self.mul_a_quantizer = quant_nn.TensorQuantizer(
              quant_nn.QuantConv2d.default_quant_desc_input
          )
          self.mul_b_quantizer = quant_nn.TensorQuantizer(
              quant_nn.QuantConv2d.default_quant_desc_input
          )
      else:
          self.mul_a_quantizer = nn.Identity()
          self.mul_b_quantizer = nn.Identity()
  
imports: import copy
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Optional
  import torch
  import warnings
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SequentialSqueezeAndExcitation object from inherited base classes: SqueezeAndExcitation with example: obj = SequentialSqueezeAndExcitation(in_channels, squeeze, activation, quantized)