code: def __init__(self, n_class=1000, input_size=224, width_mult=1.0):
      super(MobileNetV2, self).__init__()
      # setting of inverted residual blocks
      self.interverted_residual_setting = [
          # t, c, n, s
          [1, 16, 1, 1],
          [6, 24, 2, 2],
          [6, 32, 3, 2],
          [6, 64, 4, 2],
          [6, 96, 3, 1],
          [6, 160, 3, 2],
          [6, 320, 1, 1],
      ]
  
      # building first layer
      assert input_size % 32 == 0
      input_channel = int(32 * width_mult)
      self.last_channel = (
          int(1280 * width_mult) if width_mult > 1.0 else 1280
      )
      self.features = [conv_bn(3, input_channel, 2)]
      # building inverted residual blocks
      for t, c, n, s in self.interverted_residual_setting:
          output_channel = int(c * width_mult)
          for i in range(n):
              if i == 0:
                  self.features.append(
                      InvertedResidual(
                          input_channel, output_channel, s, t
                      )
                  )
              else:
                  self.features.append(
                      InvertedResidual(
                          input_channel, output_channel, 1, t
                      )
                  )
              input_channel = output_channel
      # building last several layers
      self.features.append(
          conv_1x1_bn(input_channel, self.last_channel)
      )
      self.features.append(nn.AvgPool2d(input_size // 32))
      # make it nn.Sequential
      self.features = nn.Sequential(*self.features)
  
      # building classifier
      self.classifier = nn.Sequential(
          nn.Dropout(),
          nn.Linear(self.last_channel, n_class),
      )
  
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 MobileNetV2 object from inherited base classes: nn with example: obj = MobileNetV2(n_class, input_size, width_mult)