I am using PyTorch 1.4 and need to export a model with convolutions inside a loop in `forward`: 

```
class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()

    def forward(self, x):
        for i in range(5):
            conv = torch.nn.Conv1d(1, 1, 2*i+3)
            x = torch.nn.Relu()(conv(x))
        return x

torch.jit.script(MyCell())

```

This gives the following error:

```
RuntimeError: 
Arguments for call are not valid.
The following variants are available:

  _single(float[1] x) -> (float[]):
  Expected a value of type 'List[float]' for argument 'x' but instead found type 'Tensor'.

  _single(int[1] x) -> (int[]):
  Expected a value of type 'List[int]' for argument 'x' but instead found type 'Tensor'.

The original call is:
  File "***/torch/nn/modules/conv.py", line 187
                 padding=0, dilation=1, groups=1,
                 bias=True, padding_mode='zeros'):
        kernel_size = _single(kernel_size)
                      ~~~~~~~ <--- HERE
        stride = _single(stride)
        padding = _single(padding)
'Conv1d.__init__' is being compiled since it was called from 'Conv1d'
  File "***", line ***
    def forward(self, x):
        for _ in range(5):
            conv = torch.nn.Conv1d(1, 1, 2*i+3)
                   ~~~~~~~~~~~~~~~ <--- HERE
            x = torch.nn.Relu()(conv(x))
        return x
'Conv1d' is being compiled since it was called from 'MyCell.forward'
  File "***", line ***
    def forward(self, x, h):
        for _ in range(5):
            conv = torch.nn.Conv1d(1, 1, 2*i+3)
            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
            x = torch.nn.Relu()(conv(x))
        return x

```

I have also tried pre-defining the `conv`'s then putting them in a list inside `__init__`, but such a type is not allowed by TorchScript:

```
class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.conv = [torch.nn.Conv1d(1, 1, 2*i+3) for i in range(5)]

    def forward(self, x):
        for i in range(len(self.conv)):
            x = torch.nn.Relu()(self.conv[i](x))
        return x

torch.jit.script(MyCell())

```

This instead gives:

```
RuntimeError: 
Module 'MyCell' has no attribute 'conv' (This attribute exists on the Python module, but we failed to convert Python type: 'list' to a TorchScript type.):
  File "***", line ***
    def forward(self, x):
        for i in range(len(self.conv)):
                           ~~~~~~~~~ <--- HERE
            x = torch.nn.Relu()(self.conv[i](x))
        return x

```

So how to export this module? Background: I am exporting [Mixed-scale Dense Networks (source)](https://github.com/ahendriksen/msd_pytorch/blob/master/msd_pytorch/msd_block.py#L33) to TorchScript; while `nn.Sequential` may work for this simplified case, practically I need to convolve with all the historical convolution outputs in each iteration, which is more than chaining the layers.