# Auto-extracted class source (static)

class SavantRRF_Gauge(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(SavantRRF_Gauge, self).__init__()
        self.conv1 = nn.Conv1d(input_dim, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv1d(128, 256, kernel_size=3, padding=1)
        self.dropout = nn.Dropout(0.25)
        # The input size to fc1 is based on the output size of conv3.
        # Assuming input sequence length is 160, after 3 conv layers with kernel_size 3 and padding 1,
        # the sequence length remains 160. 256 channels * 160 length = 40960.
        self.fc1 = nn.Linear(256*160, 512) # Corrected input size based on sequence_length=160
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, output_dim)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        return torch.sigmoid(self.fc3(x))