Disable warning when using with_format format on tensors
State: open | Author: Haislich (NONE)
Labels: name: enhancement

### Feature request

If we write this code: 

```python
"""Get data and define datasets."""

from enum import StrEnum
from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision import transforms


class Split(StrEnum):
    """Describes what type of split to use in the dataloader"""

    TRAIN = "train"
    TEST = "test"
    VAL = "validation"


class ImageNetDataLoader(DataLoader):
    """Create an ImageNetDataloader"""

    _preprocess_transform = transforms.Compose(
        [
            transforms.Resize(256),
            transforms.CenterCrop(224),
        ]
    )

    def __init__(self, batch_size: int = 4, split: Split = Split.TRAIN):
        dataset = (
            load_dataset(
                "imagenet-1k",
                split=split,
                trust_remote_code=True,
                streaming=True,
            )
            .with_format("torch")
            .map(self._preprocess)
        )

        super().__init__(dataset=dataset, batch_size=batch_size)

    def _preprocess(self, data):
        if data["image"].shape[0] < 3:
            data["image"] = data["image"].repeat(3, 1, 1)
        data["image"] = self._preprocess_transform(data["image"].float())
        return data


if __name__ == "__main__":

    dataloader = ImageNetDataLoader(batch_size=2)
    for batch in dataloader:
        print(batch["image"])
        break
```
This will trigger an user warning : 

```bash
datasets\formatting\torch_formatter.py:85: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```

### Motivation

This happens because the the way the formatted tensor is returned in `TorchFormatter._tensorize`.
This function handle values of different types, according to some tests it seems that possible value types are `int`, `numpy.ndarray` and `torch.Tensor`.
In particular this warning is triggered when the value type is `torch.Tensor`, because is not the suggested Pytorch way of doing it:

- https://stackoverflow.com/questions/55266154/pytorch-preferred-way-to-copy-a-tensor
- https://discuss.pytorch.org/t/it-is-recommended-to-use-source-tensor-clone-detach-or-sourcetensor-clone-detach-requires-grad-true/101218#:~:text=The%20warning%20points%20to%20wrapping%20a%20tensor%20in%20torch.tensor%2C%20which%20is%20not%20recommended.%0AInstead%20of%20torch.tensor(outputs)%20use%20outputs.clone().detach()%20or%20the%20same%20with%20.requires_grad_(True)%2C%20if%20necessary.



### Your contribution

A solution that I found to be working is to change the current way of doing it: 

```python
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```

To:

```python
if (isinstance(value, torch.Tensor)):
    tensor = value.clone().detach()
    if self.torch_tensor_kwargs.get('requires_grad', False): 
        tensor.requires_grad_()
    return tensor
else:
    return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
```