# Codeblock 8a
class MobileNetV1(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.first_conv = FirstConv()    #(1)
        
        self.depthwise_sep_conv0 = DepthwiseSeparableConv(in_channels=32, 
                                                          out_channels=64)
        
        self.depthwise_sep_conv1 = DepthwiseSeparableConv(in_channels=64, 
                                                          out_channels=128, 
                                                          downsample=True)
        
        self.depthwise_sep_conv2 = DepthwiseSeparableConv(in_channels=128, 
                                                          out_channels=128)
        
        self.depthwise_sep_conv3 = DepthwiseSeparableConv(in_channels=128, 
                                                          out_channels=256, 
                                                          downsample=True)
        
        self.depthwise_sep_conv4 = DepthwiseSeparableConv(in_channels=256, 
                                                          out_channels=256)
        
        self.depthwise_sep_conv5 = DepthwiseSeparableConv(in_channels=256, 
                                                          out_channels=512, 
                                                          downsample=True)
        
        self.depthwise_sep_conv6 = nn.ModuleList(
            [DepthwiseSeparableConv(in_channels=512, out_channels=512) for _ in range(5)]
        )
        
        self.depthwise_sep_conv7 = DepthwiseSeparableConv(in_channels=512, 
                                                          out_channels=1024, 
                                                          downsample=True)
        
        self.depthwise_sep_conv8 = DepthwiseSeparableConv(in_channels=1024,  #(2)
                                                          out_channels=1024)
        
        num_out_channels = self.depthwise_sep_conv8.pwconv.out_channels      #(3)
        
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1,1))      #(4)
        self.fc = nn.Linear(in_features=num_out_channels,           #(5)
                            out_features=NUM_CLASSES)
        self.softmax = nn.Softmax(dim=1)                            #(6)