This is the code for my CNN(I know, way too many layers). I am getting following runtime error:-
RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x6272 and 57344x7)
self.cnn_layers = nn.Sequential(

            # Defining a 2D convolution layer

            nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1),

            # Putting a 2D Batchnorm after CNN layer

            nn.BatchNorm2d(8),

            # Adding Relu Activation

            nn.ReLU(inplace=True),

            nn.Conv2d(8, 8, kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(8),

            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1),

            # Putting a 2D Batchnorm after CNN layer

            nn.BatchNorm2d(16),

            # Adding Relu Activation

            nn.ReLU(inplace=True),

            nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(16),

            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),

            # Putting a 2D Batchnorm after CNN layer

            nn.BatchNorm2d(32),

            # Adding Relu Activation

            nn.ReLU(inplace=True),

            nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(32),

            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),

            # Putting a 2D Batchnorm after CNN layer

            nn.BatchNorm2d(64),

            # Adding Relu Activation

            nn.ReLU(inplace=True),

            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(64),

            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),

            # Putting a 2D Batchnorm after CNN layer

            nn.BatchNorm2d(128),

            # Adding Relu Activation

            nn.ReLU(inplace=True),

            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(128),

            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Flatten(),

            nn.Linear(in_features=57344, out_features=7)

        )