One solution is to concatenate the tensors in the list after adding a new dimension:
    def forward(self, x):
        x = torch.cat([F.relu(conv(x[i])).unsqueeze(dim=0) for i, conv in enumerate(self.conv1)], dim=0) #//8 4 4 4 4
        x = torch.cat([F.relu(conv(x[i])).unsqueeze(dim=0) for i, conv in enumerate(self.conv2)], dim=0) #//8 4 4 4 4
        x = torch.cat([F.relu(conv(x[i])).unsqueeze(dim=0) for i, conv in enumerate(self.conv3)], dim=0) #// 8 4 4 4 4
        x = torch.cat([F.relu(conv(x[i])).unsqueeze(dim=0) for i, conv in enumerate(self.conv4)], dim=0) #//8 4 4 4 4