code: def __init__(self, inp, oup, stride, expand_ratio):
      super(InvertedResidual, self).__init__()
      self.stride = stride
      assert stride in [1, 2]
  
      self.use_res_connect = self.stride == 1 and inp == oup
  
      self.conv = nn.Sequential(
          # pw
          nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False),
          nn.BatchNorm2d(inp * expand_ratio),
          nn.ReLU6(inplace=True),
          # dw
          nn.Conv2d(
              inp * expand_ratio,
              inp * expand_ratio,
              3,
              stride,
              1,
              groups=inp * expand_ratio,
              bias=False,
          ),
          nn.BatchNorm2d(inp * expand_ratio),
          nn.ReLU6(inplace=True),
          # pw-linear
          nn.Conv2d(inp * expand_ratio, oup, 1, 1, 0, bias=False),
          nn.BatchNorm2d(oup),
      )
  
file_path: pytorch2keras/tests/models/mobilinet.py
imports: import numpy
  import torch
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch.nn
  import math
label_desc: Use image node_id 1 to create a new InvertedResidual object from inherited base classes: nn with example: obj = InvertedResidual(inp, oup, stride, expand_ratio)