t points.
        resize: Resize images to this size before computing the similarity between generated images.
        lower_discard: Lower quantile to discard from the distances, before computing the mean and standard deviation.
        upper_discard: Upper quantile to discard from the distances, before computing the mean and standard deviation.
        sim_net: Similarity network to use. Can be a `nn.Module` or one of 'alex', 'vgg', 'squeeze', where the three
            latter options correspond to the pretrained networks from the `LPIPS`_ paper.
        device: Device to use for the computation.

    Returns:
        A tuple containing the mean, standard deviation and all distances.

    Example::
        >>> from torchmetrics.functional.image import perceptual_path_length
        >>> import torch
        >>> _ = torch.manual_seed(42)
        >>> class DummyGenerator(torch.nn.Module):
        ...    def __init__(self, z_size) -> None:
        ...       super().__init__()
        ...       self.z_size = z_size
        ...       self.model = torch.nn.Sequential(torch.nn.Linear(z_size, 3*128*128), torch.nn.Sigmoid())
        ...    def forward(self, z):
        ...       return 255 * (self.model(z).reshape(-1, 3, 128, 128) + 1)
        ...    def sample(self, num_samples):
        ...      return torch.randn(num_samples, self.z_size)
        >>> generator = DummyGenerator(2)
        >>> perceptual_path_length(generator, num_samples=10)  # doctest: +SKIP
        (tensor(0.1945),
        tensor(0.1222),
        tensor([0.0990, 0.4173, 0.1628, 0.3573, 0.1875, 0.0335, 0.1095, 0.1887, 0.1953]))

    z—Metric `perceptual_path_length` requires torchvision which is not installed.Install with `pip install torchvision` or `pip install torchmetrics[image]`rB