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<code>
class block(nn.Module):
    def __init__(self, in_channels, out_channels, identity_downsample=None, stride=1):
        super(block, self).__init__()
        self.out_channels, self.in_channels = out_channels, in_channels
        self.expansion = 1
        self.conv1 = nn.Conv1d(self.in_channels, self.out_channels, kernel_size=15, stride=stride, padding=7)
        self.bn1 = nn.BatchNorm1d(self.out_channels)
        self.conv2 = nn.Conv1d(self.out_channels, self.out_channels*2, kernel_size=9, stride=1, padding=4)
        self.bn2 = nn.BatchNorm1d(self.out_channels*2)
        self.conv3 = nn.Conv1d(self.out_channels*2, self.out_channels*2, kernel_size=5, stride=1, padding=2)
        self.bn3 = nn.BatchNorm1d(self.out_channels*2)
        self.relu = nn.ReLU()
        self.identity_downsample = identity_downsample        
        self.lin1 = nn.Linear(out_channels*2*250, 800)
        self.lin2 = nn.Linear(800, 40)
        self.lin3 = nn.Linear(40, 10)
        self.lin4 = nn.Linear(10, 2)

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)  
        if self.identity_downsample is not None:
          identity = self.identity_downsample(identity)
        x += identity
        return x
</code>

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