T5 has no module  ```torch_xla``` when using T5 fine-tuned on SQUADv2
# 🐛 Bug

## Information

I get this error:
``` ModuleNotFoundError: No module named 'torch_xla'```
Full error message:

```
      2 
      3 tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-squadv2")
----> 4 model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-squadv2")
      5 
      6 def get_answer(question, context):

1 frames
/usr/local/lib/python3.6/dist-packages/transformers/modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    796 
    797         if hasattr(config, "xla_device") and config.xla_device:
--> 798             import torch_xla.core.xla_model as xm
    799 
    800             model = xm.send_cpu_data_to_device(model, xm.xla_device())

ModuleNotFoundError: No module named 'torch_xla'
```

## To reproduce
```python
from transformers import AutoModelWithLMHead, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-squadv2")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-squadv2")

def get_answer(question, context):
  input_text = "question: %s  context: %s </s>" % (question, context)
  features = tokenizer([input_text], return_tensors='pt')

  output = model.generate(input_ids=features['input_ids'], 
               attention_mask=features['attention_mask'])

  return tokenizer.decode(output[0])

context = "Manuel have created RuPERTa-base with the support of HF-Transformers and Google"
question = "Who has supported Manuel?"

get_answer(question, context)
```
I used this example code a few weeks ago and had no problem...
Indeed, it seems this model was once trained or initialized on TPU. Thanks for letting us know, I'm patching it in #5636.<|||||>Yes, it was trained on TPU<|||||>It should be fixed on master now, can you try pulling from master and running your code?<|||||>I already did it and it works!!!
Thank you!!