def validate_explanation(explanation: list[str] | None) -> list[str] | None:
        """Validate the explanations for a batch.

        This method validates lists of explanation strings. It handles:
            - Type conversion to strings
            - List validation

        Args:
            explanation (``list[str]`` | ``None``): Input list of explanations.

        Returns:
            ``list[str]`` | ``None``: Validated list of explanations, or ``None``.

        Raises:
            TypeError: If ``explanation`` is not a list.

        Examples:
            Validate list of explanations::

                >>> explanations = ["The image has a crack.", "The image has a dent."]
                >>> validated = NumpyImageBatchValidator.validate_explanation(explanations)
                >>> validated
                ['The image has a crack.', 'The image has a dent.']
        """
        if explanation is None:
            return None
        if not isinstance(explanation, list):
            msg = f"Explanation must be a list of strings, got {type(explanation)}."
            raise TypeError(msg)
        return [str(exp) for exp in explanation]
Validate the explanations for a batch.

        This method validates lists of explanation strings. It handles:
            - Type conversion to strings
            - List validation

        Args:
            explanation (``list[str]`` | ``None``): Input list of explanations.

        Returns:
            ``list[str]`` | ``None``: Validated list of explanations, or ``None``.

        Raises:
            TypeError: If ``explanation`` is not a list.

        Examples:
            Validate list of explanations::

                >>> explanations = ["The image has a crack.", "The image has a dent."]
                >>> validated = NumpyImageBatchValidator.validate_explanation(explanations)
                >>> validated
                ['The image has a crack.', 'The image has a dent.']
        
src/anomalib/data/validators/numpy/image.py