Repository: pytorch
Issue: named tensor INTERNAL ASSERT FAILED when indexing with a list.
Labels: triaged,module: named tensor

## 🐛 Bug

We get a internal assertion failure when trying to index named tensors like this: `named_tensor[[0]]`.
The error message says `RuntimeError: false INTERNAL ASSERT FAILED` and `Unknown DispatchKey: Named`

## To Reproduce
Here is the output from an interactive python session:
```
(nn) jbss@rig1:~$ python
Python 3.8.3 | packaged by conda-forge | (default, Jun  1 2020, 17:43:00)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> named_tensor = torch.randn(3, names=('X',))
<stdin>:1: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /opt/conda/conda-bld/pytorch_1595629411241/work/c10/core/TensorImpl.h:840.)
>>> named_tensor[[0]]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: false INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1595629411241/work/c10/core/TensorOptions.h":659, please report a bug to PyTorch. Unknown DispatchKey: Named
>>> torch.__version__
'1.6.0'
```

## Expected behavior

No internal assertion failure.

## Environment
```
Collecting environment information...
PyTorch version: 1.6.0
Is debug build: False
CUDA used to build PyTorch: 10.1

OS: Ubuntu 20.04.1 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-10ubuntu2) 9.3.0
Clang version: Could not collect
CMake version: Could not collect

Python version: 3.8 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080
GPU 1: GeForce RTX 2080

Nvidia driver version: 435.21
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.19.1
[pip3] torch==1.6.0
[pip3] torchfile==0.1.0
[pip3] torchnet==0.0.4
[pip3] torchtest==0.5
[pip3] torchvision==0.7.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               10.1.243             h6bb024c_0
[conda] libblas                   3.8.0                    16_mkl    conda-forge
[conda] libcblas                  3.8.0                    16_mkl    conda-forge
[conda] liblapack                 3.8.0                    16_mkl    conda-forge
[conda] mkl                       2020.1                      217
[conda] mkl-service               2.3.0            py38he904b0f_0
[conda] mkl_fft                   1.1.0            py38h23d657b_0
[conda] mkl_random                1.1.1            py38h0573a6f_0
[conda] numpy                     1.19.1           py38h8854b6b_0    conda-forge
[conda] numpy-base                1.18.5           py38hde5b4d6_0
[conda] pytorch                   1.6.0           py3.8_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] torchfile                 0.1.0                    pypi_0    pypi
[conda] torchnet                  0.0.4                    pypi_0    pypi
[conda] torchtest                 0.5                      pypi_0    pypi
[conda] torchvision               0.7.0                py38_cu101    pytorch
```

cc @zou3519