code: def __init__(self, num_classes=1000):
      super(BNInception, self).__init__()
      inplace = True
      self.conv1_7x7_s2 = nn.Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)
      )
      self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, affine=True)
      self.conv1_relu_7x7 = nn.ReLU(inplace)
      self.pool1_3x3_s2 = nn.MaxPool2d(
          (3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True
      )
      self.conv2_3x3_reduce = nn.Conv2d(
          64, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.conv2_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
      self.conv2_relu_3x3_reduce = nn.ReLU(inplace)
      self.conv2_3x3 = nn.Conv2d(
          64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.conv2_3x3_bn = nn.BatchNorm2d(192, affine=True)
      self.conv2_relu_3x3 = nn.ReLU(inplace)
      self.pool2_3x3_s2 = nn.MaxPool2d(
          (3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True
      )
      self.inception_3a_1x1 = nn.Conv2d(
          192, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3a_1x1_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3a_relu_1x1 = nn.ReLU(inplace)
      self.inception_3a_3x3_reduce = nn.Conv2d(
          192, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3a_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_3a_3x3 = nn.Conv2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3a_relu_3x3 = nn.ReLU(inplace)
      self.inception_3a_double_3x3_reduce = nn.Conv2d(
          192, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3a_double_3x3_reduce_bn = nn.BatchNorm2d(
          64, affine=True
      )
      self.inception_3a_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_3a_double_3x3_1 = nn.Conv2d(
          64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3a_double_3x3_1_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3a_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_3a_double_3x3_2 = nn.Conv2d(
          96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3a_double_3x3_2_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3a_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_3a_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_3a_pool_proj = nn.Conv2d(
          192, 32, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3a_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
      self.inception_3a_relu_pool_proj = nn.ReLU(inplace)
      self.inception_3b_1x1 = nn.Conv2d(
          256, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3b_1x1_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3b_relu_1x1 = nn.ReLU(inplace)
      self.inception_3b_3x3_reduce = nn.Conv2d(
          256, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3b_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3b_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_3b_3x3 = nn.Conv2d(
          64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3b_3x3_bn = nn.BatchNorm2d(96, affine=True)
      self.inception_3b_relu_3x3 = nn.ReLU(inplace)
      self.inception_3b_double_3x3_reduce = nn.Conv2d(
          256, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3b_double_3x3_reduce_bn = nn.BatchNorm2d(
          64, affine=True
      )
      self.inception_3b_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_3b_double_3x3_1 = nn.Conv2d(
          64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3b_double_3x3_1_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3b_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_3b_double_3x3_2 = nn.Conv2d(
          96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3b_double_3x3_2_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3b_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_3b_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_3b_pool_proj = nn.Conv2d(
          256, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3b_pool_proj_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_3b_relu_pool_proj = nn.ReLU(inplace)
      self.inception_3c_3x3_reduce = nn.Conv2d(
          320, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_3c_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_3c_3x3 = nn.Conv2d(
          128, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
      )
      self.inception_3c_3x3_bn = nn.BatchNorm2d(160, affine=True)
      self.inception_3c_relu_3x3 = nn.ReLU(inplace)
      self.inception_3c_double_3x3_reduce = nn.Conv2d(
          320, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_3c_double_3x3_reduce_bn = nn.BatchNorm2d(
          64, affine=True
      )
      self.inception_3c_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_3c_double_3x3_1 = nn.Conv2d(
          64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_3c_double_3x3_1_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3c_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_3c_double_3x3_2 = nn.Conv2d(
          96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
      )
      self.inception_3c_double_3x3_2_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_3c_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_3c_pool = nn.MaxPool2d(
          (3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True
      )
      self.inception_4a_1x1 = nn.Conv2d(
          576, 224, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4a_1x1_bn = nn.BatchNorm2d(224, affine=True)
      self.inception_4a_relu_1x1 = nn.ReLU(inplace)
      self.inception_4a_3x3_reduce = nn.Conv2d(
          576, 64, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
      self.inception_4a_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_4a_3x3 = nn.Conv2d(
          64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4a_3x3_bn = nn.BatchNorm2d(96, affine=True)
      self.inception_4a_relu_3x3 = nn.ReLU(inplace)
      self.inception_4a_double_3x3_reduce = nn.Conv2d(
          576, 96, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4a_double_3x3_reduce_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_4a_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_4a_double_3x3_1 = nn.Conv2d(
          96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4a_double_3x3_1_bn = nn.BatchNorm2d(
          128, affine=True
      )
      self.inception_4a_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_4a_double_3x3_2 = nn.Conv2d(
          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4a_double_3x3_2_bn = nn.BatchNorm2d(
          128, affine=True
      )
      self.inception_4a_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_4a_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_4a_pool_proj = nn.Conv2d(
          576, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4a_relu_pool_proj = nn.ReLU(inplace)
      self.inception_4b_1x1 = nn.Conv2d(
          576, 192, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4b_1x1_bn = nn.BatchNorm2d(192, affine=True)
      self.inception_4b_relu_1x1 = nn.ReLU(inplace)
      self.inception_4b_3x3_reduce = nn.Conv2d(
          576, 96, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4b_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
      self.inception_4b_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_4b_3x3 = nn.Conv2d(
          96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4b_3x3_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4b_relu_3x3 = nn.ReLU(inplace)
      self.inception_4b_double_3x3_reduce = nn.Conv2d(
          576, 96, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4b_double_3x3_reduce_bn = nn.BatchNorm2d(
          96, affine=True
      )
      self.inception_4b_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_4b_double_3x3_1 = nn.Conv2d(
          96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4b_double_3x3_1_bn = nn.BatchNorm2d(
          128, affine=True
      )
      self.inception_4b_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_4b_double_3x3_2 = nn.Conv2d(
          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4b_double_3x3_2_bn = nn.BatchNorm2d(
          128, affine=True
      )
      self.inception_4b_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_4b_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_4b_pool_proj = nn.Conv2d(
          576, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4b_relu_pool_proj = nn.ReLU(inplace)
      self.inception_4c_1x1 = nn.Conv2d(
          576, 160, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4c_1x1_bn = nn.BatchNorm2d(160, affine=True)
      self.inception_4c_relu_1x1 = nn.ReLU(inplace)
      self.inception_4c_3x3_reduce = nn.Conv2d(
          576, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4c_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_4c_3x3 = nn.Conv2d(
          128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4c_3x3_bn = nn.BatchNorm2d(160, affine=True)
      self.inception_4c_relu_3x3 = nn.ReLU(inplace)
      self.inception_4c_double_3x3_reduce = nn.Conv2d(
          576, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4c_double_3x3_reduce_bn = nn.BatchNorm2d(
          128, affine=True
      )
      self.inception_4c_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_4c_double_3x3_1 = nn.Conv2d(
          128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4c_double_3x3_1_bn = nn.BatchNorm2d(
          160, affine=True
      )
      self.inception_4c_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_4c_double_3x3_2 = nn.Conv2d(
          160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4c_double_3x3_2_bn = nn.BatchNorm2d(
          160, affine=True
      )
      self.inception_4c_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_4c_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_4c_pool_proj = nn.Conv2d(
          576, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4c_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4c_relu_pool_proj = nn.ReLU(inplace)
      self.inception_4d_1x1 = nn.Conv2d(
          608, 96, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4d_1x1_bn = nn.BatchNorm2d(96, affine=True)
      self.inception_4d_relu_1x1 = nn.ReLU(inplace)
      self.inception_4d_3x3_reduce = nn.Conv2d(
          608, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4d_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4d_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_4d_3x3 = nn.Conv2d(
          128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4d_3x3_bn = nn.BatchNorm2d(192, affine=True)
      self.inception_4d_relu_3x3 = nn.ReLU(inplace)
      self.inception_4d_double_3x3_reduce = nn.Conv2d(
          608, 160, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4d_double_3x3_reduce_bn = nn.BatchNorm2d(
          160, affine=True
      )
      self.inception_4d_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_4d_double_3x3_1 = nn.Conv2d(
          160, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4d_double_3x3_1_bn = nn.BatchNorm2d(
          192, affine=True
      )
      self.inception_4d_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_4d_double_3x3_2 = nn.Conv2d(
          192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4d_double_3x3_2_bn = nn.BatchNorm2d(
          192, affine=True
      )
      self.inception_4d_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_4d_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_4d_pool_proj = nn.Conv2d(
          608, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4d_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4d_relu_pool_proj = nn.ReLU(inplace)
      self.inception_4e_3x3_reduce = nn.Conv2d(
          608, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4e_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_4e_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_4e_3x3 = nn.Conv2d(
          128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
      )
      self.inception_4e_3x3_bn = nn.BatchNorm2d(192, affine=True)
      self.inception_4e_relu_3x3 = nn.ReLU(inplace)
      self.inception_4e_double_3x3_reduce = nn.Conv2d(
          608, 192, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_4e_double_3x3_reduce_bn = nn.BatchNorm2d(
          192, affine=True
      )
      self.inception_4e_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_4e_double_3x3_1 = nn.Conv2d(
          192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_4e_double_3x3_1_bn = nn.BatchNorm2d(
          256, affine=True
      )
      self.inception_4e_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_4e_double_3x3_2 = nn.Conv2d(
          256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
      )
      self.inception_4e_double_3x3_2_bn = nn.BatchNorm2d(
          256, affine=True
      )
      self.inception_4e_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_4e_pool = nn.MaxPool2d(
          (3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True
      )
      self.inception_5a_1x1 = nn.Conv2d(
          1056, 352, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5a_1x1_bn = nn.BatchNorm2d(352, affine=True)
      self.inception_5a_relu_1x1 = nn.ReLU(inplace)
      self.inception_5a_3x3_reduce = nn.Conv2d(
          1056, 192, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5a_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)
      self.inception_5a_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_5a_3x3 = nn.Conv2d(
          192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5a_3x3_bn = nn.BatchNorm2d(320, affine=True)
      self.inception_5a_relu_3x3 = nn.ReLU(inplace)
      self.inception_5a_double_3x3_reduce = nn.Conv2d(
          1056, 160, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5a_double_3x3_reduce_bn = nn.BatchNorm2d(
          160, affine=True
      )
      self.inception_5a_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_5a_double_3x3_1 = nn.Conv2d(
          160, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5a_double_3x3_1_bn = nn.BatchNorm2d(
          224, affine=True
      )
      self.inception_5a_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_5a_double_3x3_2 = nn.Conv2d(
          224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5a_double_3x3_2_bn = nn.BatchNorm2d(
          224, affine=True
      )
      self.inception_5a_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_5a_pool = nn.AvgPool2d(
          3, stride=1, padding=1, ceil_mode=True, count_include_pad=True
      )
      self.inception_5a_pool_proj = nn.Conv2d(
          1056, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_5a_relu_pool_proj = nn.ReLU(inplace)
      self.inception_5b_1x1 = nn.Conv2d(
          1024, 352, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5b_1x1_bn = nn.BatchNorm2d(352, affine=True)
      self.inception_5b_relu_1x1 = nn.ReLU(inplace)
      self.inception_5b_3x3_reduce = nn.Conv2d(
          1024, 192, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5b_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)
      self.inception_5b_relu_3x3_reduce = nn.ReLU(inplace)
      self.inception_5b_3x3 = nn.Conv2d(
          192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5b_3x3_bn = nn.BatchNorm2d(320, affine=True)
      self.inception_5b_relu_3x3 = nn.ReLU(inplace)
      self.inception_5b_double_3x3_reduce = nn.Conv2d(
          1024, 192, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5b_double_3x3_reduce_bn = nn.BatchNorm2d(
          192, affine=True
      )
      self.inception_5b_relu_double_3x3_reduce = nn.ReLU(inplace)
      self.inception_5b_double_3x3_1 = nn.Conv2d(
          192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5b_double_3x3_1_bn = nn.BatchNorm2d(
          224, affine=True
      )
      self.inception_5b_relu_double_3x3_1 = nn.ReLU(inplace)
      self.inception_5b_double_3x3_2 = nn.Conv2d(
          224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
      )
      self.inception_5b_double_3x3_2_bn = nn.BatchNorm2d(
          224, affine=True
      )
      self.inception_5b_relu_double_3x3_2 = nn.ReLU(inplace)
      self.inception_5b_pool = nn.MaxPool2d(
          (3, 3),
          stride=(1, 1),
          padding=(1, 1),
          dilation=(1, 1),
          ceil_mode=True,
      )
      self.inception_5b_pool_proj = nn.Conv2d(
          1024, 128, kernel_size=(1, 1), stride=(1, 1)
      )
      self.inception_5b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
      self.inception_5b_relu_pool_proj = nn.ReLU(inplace)
      self.last_linear = nn.Linear(1024, num_classes)
  
file_path: pywick/pywick/models/classification/bn_inception.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new BNInception object from inherited base classes: nn with example: obj = BNInception(num_classes)