# Codeblock 3
class Conv(nn.Module):
    def __init__(self, first=False):      #(1)
        super().__init__()
        
        if first:
            in_channels = 3               #(2)
            out_channels = int(32*WIDTH_MULTIPLIER)          #(3)
            kernel_size = 3               #(4)
            stride = 2                    #(5)
            padding = 1                   #(6)
        else:
            in_channels  = int(320*WIDTH_MULTIPLIER)         #(7)
            out_channels = int(1280*WIDTH_MULTIPLIER)        #(8)
            kernel_size = 1               #(9)
            stride = 1                    #(10)
            padding = 0                   #(11)
        
        self.conv = nn.Conv2d(in_channels=in_channels,       #(12)
                              out_channels=out_channels, 
                              kernel_size=kernel_size,
                              stride=stride, 
                              padding=padding, 
                              bias=False)
        self.bn = nn.BatchNorm2d(num_features=out_channels)  #(13)
        self.relu6 = nn.ReLU6()           #(14)
    
    def forward(self, x):
        x = self.relu6(self.bn(self.conv(x)))                #(15)
        return x