def METHOD_NAME(
    self, x: "torch.Tensor", y: Optional[Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]] = None
) -> Tuple["torch.Tensor", Optional[Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]]]:
    """
    Apply transformations to inputs `x` and labels `y`.
    :param x: Input samples.
    :param y: Label of the samples `x`.
    :return: Transformed samples and labels.
    """
    import torch
    x_preprocess_list = []
    y_preprocess_list_classification: List[torch.Tensor] = []
    y_preprocess_list_object_detection: List[List[Dict[str, torch.Tensor]]] = []
    for i_image in range(x.shape[0]):
        for _ in range(self.nb_samples):
            x_i = x[[i_image]]
            y_i: Optional[Union[torch.Tensor, List[Dict[str, torch.Tensor]]]]
            if y is not None:
                if isinstance(y, list):
                    y_i = [y[i_image]]
                else:
                    y_i = y[[i_image]]
            else:
                y_i = None
            x_preprocess, y_preprocess_i = self._transform(x_i, y_i)
            x_preprocess_list.append(torch.squeeze(x_preprocess, dim=0))
            if y is not None and y_preprocess_i is not None:
                if isinstance(y_preprocess_i, torch.Tensor):
                    y_preprocess_list_classification.append(torch.squeeze(y_preprocess_i, dim=0))
                else:
                    y_preprocess_list_object_detection.append(y_preprocess_i)
    x_preprocess = torch.stack(x_preprocess_list, dim=0)
    y_preprocess: Optional[Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]]
    if y is None:
        y_preprocess = y
    else:
        if isinstance(y, torch.Tensor):
            y_preprocess = torch.stack(y_preprocess_list_classification, dim=0)
        else:
            y_preprocess = [item for sublist in y_preprocess_list_object_detection for item in sublist]
    return x_preprocess, y_preprocess