text: import math
  
  import torch
  
  
  class TorchModel(torch.nn.Module):
      def __init__(self):
          super().__init__()
          INPUT_CHANNELS: int = 4
          CONV_NUM_FILTERS: int = 1
          CONV_FILTER_WIDTH: int = 3
          OUTPUT_UNITS: int = 2
  
          self.conv = torch.nn.Sequential(
              torch.nn.Conv1d(INPUT_CHANNELS,
                              CONV_NUM_FILTERS,
                              CONV_FILTER_WIDTH, 1,
                              math.floor(CONV_FILTER_WIDTH / 2)),
              torch.nn.ReLU(),
              torch.nn.AdaptiveMaxPool1d(1)
          )
  
          self.fc = torch.nn.Sequential(
              torch.nn.Linear(CONV_NUM_FILTERS, OUTPUT_UNITS)
          )
  
      def forward(self, x):
          batch_size = x.size(0)
          x = self.conv(x)
          x = x.view(batch_size, -1)
          x = self.fc(x)
          return x
  