Add a condition for nested_detach
Author: haikuoxin
Pull request: yes

# What does this PR do?

```python
def nested_detach(tensors):
    "Detach `tensors` (even if it's a nested list/tuple/dict of tensors)."
    if isinstance(tensors, (list, tuple)):
        return type(tensors)(nested_detach(t) for t in tensors)
    elif isinstance(tensors, Mapping):
        return type(tensors)({k: nested_detach(t) for k, t in tensors.items()})
    return tensors.detach() if isinstance(tensors, torch.Tensor) else tensors
```

The reason is that Trainer predict_step executes the nested_detach function to ensure that every value in inputs has been detached. One assumption here is that all model inputs have been converted to tensors.

However, the output of the dataset of some model projects is not a tensor, but a string. If the input strings is converted into a tensors through tokenizer in the forward function, it will be problematic to perform detach on the string. so adding a condition to fixing it.

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Fixes #31852


## Before submitting
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## Who can review?
@muellerzr 

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Comments:
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