class_docstr: Args
class_docstr_tok: ["Args"]
code: def __init__(self, n_class=1000, input_size=224, width_mult=1.0):
      super().__init__()
      block = InvertedResidual
      input_channel = 32
      last_channel = 1280
      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 = make_divisible(input_channel * width_mult)  # first channel is always 32!
      self.last_channel = (
          make_divisible(last_channel * width_mult)
          if width_mult > 1.0
          else last_channel
      )
      self.features = [conv_bn(3, input_channel, 2)]
      # building inverted residual blocks
      for t, c, n, s in interverted_residual_setting:
          output_channel = (
              make_divisible(c * width_mult) if t > 1 else c
          )
          for i in range(n):
              if i == 0:
                  self.features.append(
                      block(
                          input_channel,
                          output_channel,
                          s,
                          expand_ratio=t,
                      )
                  )
              else:
                  self.features.append(
                      block(
                          input_channel,
                          output_channel,
                          1,
                          expand_ratio=t,
                      )
                  )
              input_channel = output_channel
      # building last several layers
      self.features.append(
          conv_1x1_bn(input_channel, self.last_channel)
      )
      # make it nn.Sequential
      self.features = nn.Sequential(*self.features)
  
      # building classifier
      self.classifier = nn.Linear(self.last_channel, n_class)
  
      self._initialize_weights()
  
file_path: towhee/towhee/models/tsm/mobilenet_v2.py
imports: import math
  import numpy
  from torch import nn
  from torch.utils.model_zoo import load_url
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)