def validate_original_image(original_image: np.ndarray | None) -> np.ndarray | None:
        """Validate the original video batch.

        Args:
            original_image (``np.ndarray`` | ``None``): Input original video batch to
                validate.

        Returns:
            ``np.ndarray`` | ``None``: Validated original video batch, or None if input is
                None.

        Raises:
            TypeError: If the input is not a numpy array.
            ValueError: If the original image batch shape is invalid.

        Example:
            >>> import numpy as np
            >>> validator = NumpyVideoBatchValidator()
            >>> original_batch = np.random.rand(2, 10, 224, 224, 3)  # [N, T, H, W, C]
            >>> validated_batch = validator.validate_original_image(original_batch)
            >>> print(validated_batch.shape, validated_batch.dtype)
            (2, 10, 224, 224, 3) float64
        """
        if original_image is None:
            return None
        if not isinstance(original_image, np.ndarray):
            msg = f"Original image batch must be a numpy.ndarray, got {type(original_image)}."
            raise TypeError(msg)
        if original_image.ndim not in {4, 5}:
            msg = (
                "Original image batch must have shape [N, T, H, W, C] or [N, H, W, C], "
                f"got shape {original_image.shape}."
            )
            raise ValueError(msg)
        if original_image.shape[-1] != 3:
            msg = f"Original image batch must have 3 channels, got {original_image.shape[-1]}."
            raise ValueError(msg)
        return original_image
Validate the original video batch.

        Args:
            original_image (``np.ndarray`` | ``None``): Input original video batch to
                validate.

        Returns:
            ``np.ndarray`` | ``None``: Validated original video batch, or None if input is
                None.

        Raises:
            TypeError: If the input is not a numpy array.
            ValueError: If the original image batch shape is invalid.

        Example:
            >>> import numpy as np
            >>> validator = NumpyVideoBatchValidator()
            >>> original_batch = np.random.rand(2, 10, 224, 224, 3)  # [N, T, H, W, C]
            >>> validated_batch = validator.validate_original_image(original_batch)
            >>> print(validated_batch.shape, validated_batch.dtype)
            (2, 10, 224, 224, 3) float64
        
src/anomalib/data/validators/numpy/video.py