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# Source code for torchvision.transforms.v2._deprecated

    import warnings
    from typing import Any, Dict, Union

    import numpy as np
    import PIL.Image
    import torch
    from torchvision.transforms import functional as _F

    from torchvision.transforms.v2 import Transform

    class ToTensor(Transform):
        """[BETA] [DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`` instead.

        Convert a PIL Image or ndarray to tensor and scale the values accordingly.

        .. v2betastatus:: ToTensor transform

        .. warning::
            :class:`v2.ToTensor` is deprecated and will be removed in a future release.
            Please use instead ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])``.

        This transform does not support torchscript.

        Converts a PIL Image or numpy.ndarray (H x W x C) in the range
        [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
        if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
        or if the numpy.ndarray has dtype = np.uint8

        In the other cases, tensors are returned without scaling.

        .. note::
            Because the input image is scaled to [0.0, 1.0], this transformation should not be used when
            transforming target image masks. See the `references`_ for implementing the transforms for image masks.

        .. _references: https://github.com/pytorch/vision/tree/main/references/segmentation
        """

        _transformed_types = (PIL.Image.Image, np.ndarray)

        def __init__(self) -> None:
            warnings.warn(
                "The transform `ToTensor()` is deprecated and will be removed in a future release. "
                "Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`."
            )
            super().__init__()

        def _transform(self, inpt: Union[PIL.Image.Image, np.ndarray], params: Dict[str, Any]) -> torch.Tensor:
            return _F.to_tensor(inpt)

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