Repository: pytorch
Issue: nn.Bilinear cannot be used inside nn.Sequential
Labels: feature,module: nn,triaged

## 🐛 Bug

Using `nn.Bilinear` inside a `nn.Sequential` raises a `TypeError` because `nn.Sequential` doesn't support multiple inputs.

## To Reproduce

Steps to reproduce the behavior:

```python
import torch
bilinear = torch.nn.Bilinear(4, 8, 12)
bilinear_seq = torch.nn.Sequential(bilinear)

input_1 = torch.zeros((10, 4))
input_2 = torch.zeros((10, 8))

print(f'bilinear output dims: {bilinear(input_1, input_2).shape}')
print(f'sequential bilinear output dims: {bilinear_seq(input_1, input_2).shape}')
```
Output:
```bash
bilinear output dims: torch.Size([10, 12])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
 in 
      9 print(f'expected output dims:\n{(10, 12)}')
     10 print(f'bilinear output dims:\n{bilinear(input_1, input_2).shape}')
---> 11 print(f'sequential bilinear output dims:\n{bilinear_seq(input_1, input_2).shape}')

~/anaconda3/envs/ae/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    538             result = self._slow_forward(*input, **kwargs)
    539         else:
--> 540             result = self.forward(*input, **kwargs)
    541         for hook in self._forward_hooks.values():
    542             hook_result = hook(self, input, result)

TypeError: forward() takes 2 positional arguments but 3 were given
```
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->

## Expected behavior

One would expect `nn.Sequential` to work with any number of inputs as long as the inputs are passed correctly from one module to the next one inside `nn.Sequential`.

## Environment

PyTorch version: 1.5.0.dev20200210+cu100
Is debug build: No
CUDA used to build PyTorch: 10.0

OS: Ubuntu 18.10
GCC version: (Ubuntu 8.3.0-6ubuntu1~18.10.1) 8.3.0
CMake version: version 3.12.1

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.168
GPU models and configuration: 
GPU 0: TITAN X (Pascal)
GPU 1: GeForce RTX 2080

Nvidia driver version: 418.67
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.16.4
[conda] _pytorch_select           0.2                       gpu_0  
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               10.0.130                      0  
[conda] mkl                       2019.4                      243  
[conda] mkl-service               2.3.0            py37he904b0f_0  
[conda] mkl_fft                   1.0.15           py37ha843d7b_0  
[conda] mkl_random                1.1.0            py37hd6b4f25_0  
[conda] numpy                     1.17.4           py37hc1035e2_0  
[conda] numpy-base                1.17.4           py37hde5b4d6_0  
[conda] torch                     1.5.0.dev20200210+cu100          pypi_0    pypi
[conda] torch-nightly             1.2.0.dev20190805          pypi_0    pypi
[conda] torchsnooper              0.7.1                    pypi_0    pypi
[conda] torchvision               0.6.0.dev20200210+cu100          pypi_0    pypi

## Additional context

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