def get_activations(
        self,
        x: np.ndarray,
        layer: int | str | None = None,
        batch_size: int = 128,
        framework: bool = False,
    ) -> np.ndarray:
        """
        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
        calling `layer_names`.

        :param x: Input for computing the activations.
        :param layer: Layer for computing the activations
        :param batch_size: Size of batches.
        :param framework: If true, return the intermediate tensor representation of the activation.
        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
        """
        raise NotImplementedError

        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
        calling `layer_names`.

        :param x: Input for computing the activations.
        :param layer: Layer for computing the activations
        :param batch_size: Size of batches.
        :param framework: If true, return the intermediate tensor representation of the activation.
        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
        
art/experimental/estimators/classification/jax.py