src/sagemaker/automl/automl.py [683:793]:
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            )

    @classmethod
    def _get_supported_inference_keys(cls, container, default=None):
        """Returns the inference keys supported by the container.

        Args:
            container (dict): Dictionary representing container
            default (object): The value to be returned if the container definition
                              has no marker environment variable

        Returns:
            List of keys the container support or default

        Raises:
            KeyError if the default is None and the container definition has
            no marker environment variable SAGEMAKER_INFERENCE_SUPPORTED.
        """
        try:
            return [
                x.strip()
                for x in container["Environment"]["SAGEMAKER_INFERENCE_SUPPORTED"].split(",")
            ]
        except KeyError:
            if default is None:
                raise
        return default

    @classmethod
    def _check_inference_keys(cls, inference_response_keys, containers):
        """Checks if the pipeline supports the inference keys for the containers.

        Given inference response keys and list of containers, determines whether
        the keys are supported.

        Args:
            inference_response_keys (list): List of keys for inference response content.
            containers (list): list of inference container.

        Raises:
            ValueError, if one or more keys in inference_response_keys are not supported
            the inference pipeline.
        """
        if not inference_response_keys:
            return
        try:
            supported_inference_keys = cls._get_supported_inference_keys(container=containers[-1])
        except KeyError:
            raise ValueError(
                "The inference model does not support selection of inference content beyond "
                "it's default content. Please retry without setting "
                "inference_response_keys key word argument."
            )
        bad_keys = []
        for key in inference_response_keys:
            if key not in supported_inference_keys:
                bad_keys.append(key)

        if bad_keys:
            raise ValueError(
                "Requested inference output keys [{bad_keys_str}] are unsupported. "
                "The supported inference keys are [{allowed_keys_str}]".format(
                    bad_keys_str=", ".join(bad_keys),
                    allowed_keys_str=", ".join(supported_inference_keys),
                )
            )

    @classmethod
    def validate_and_update_inference_response(cls, inference_containers, inference_response_keys):
        """Validates the requested inference keys and updates response content.

        On validation, also updates the inference containers to emit appropriate response
        content in the inference response.

        Args:
            inference_containers (list): list of inference containers
            inference_response_keys (list): list of inference response keys

        Raises:
            ValueError: if one or more of inference_response_keys are unsupported by the model
        """
        if not inference_response_keys:
            return

        cls._check_inference_keys(inference_response_keys, inference_containers)

        previous_container_output = None

        for container in inference_containers:
            supported_inference_keys_container = cls._get_supported_inference_keys(
                container, default=[]
            )
            if not supported_inference_keys_container:
                previous_container_output = None
                continue
            current_container_output = None
            for key in inference_response_keys:
                if key in supported_inference_keys_container:
                    current_container_output = (
                        current_container_output + "," + key if current_container_output else key
                    )

            if previous_container_output:
                container["Environment"].update(
                    {"SAGEMAKER_INFERENCE_INPUT": previous_container_output}
                )
            if current_container_output:
                container["Environment"].update(
                    {"SAGEMAKER_INFERENCE_OUTPUT": current_container_output}
                )
            previous_container_output = current_container_output
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src/sagemaker/automl/automlv2.py [1222:1332]:
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            )

    @classmethod
    def _get_supported_inference_keys(cls, container, default=None):
        """Returns the inference keys supported by the container.

        Args:
            container (dict): Dictionary representing container
            default (object): The value to be returned if the container definition
                              has no marker environment variable

        Returns:
            List of inference keys the container support or default

        Raises:
            KeyError if the default is None and the container definition has
            no marker environment variable SAGEMAKER_INFERENCE_SUPPORTED.
        """
        try:
            return [
                x.strip()
                for x in container["Environment"]["SAGEMAKER_INFERENCE_SUPPORTED"].split(",")
            ]
        except KeyError:
            if default is None:
                raise
        return default

    @classmethod
    def _check_inference_keys(cls, inference_response_keys, containers):
        """Checks if the pipeline supports the inference keys for the containers.

        Given inference response keys and list of containers, determines whether
        the keys are supported.

        Args:
            inference_response_keys (list): List of keys for inference response content.
            containers (list): list of inference container.

        Raises:
            ValueError, if one or more keys in inference_response_keys are not supported
            the inference pipeline.
        """
        if not inference_response_keys:
            return
        try:
            supported_inference_keys = cls._get_supported_inference_keys(container=containers[-1])
        except KeyError:
            raise ValueError(
                "The inference model does not support selection of inference content beyond "
                "it's default content. Please retry without setting "
                "inference_response_keys key word argument."
            )
        bad_keys = []
        for key in inference_response_keys:
            if key not in supported_inference_keys:
                bad_keys.append(key)

        if bad_keys:
            raise ValueError(
                "Requested inference output keys [{bad_keys_str}] are unsupported. "
                "The supported inference keys are [{allowed_keys_str}]".format(
                    bad_keys_str=", ".join(bad_keys),
                    allowed_keys_str=", ".join(supported_inference_keys),
                )
            )

    @classmethod
    def validate_and_update_inference_response(cls, inference_containers, inference_response_keys):
        """Validates the requested inference keys and updates response content.

        On validation, also updates the inference containers to emit appropriate response
        content in the inference response.

        Args:
            inference_containers (list): list of inference containers
            inference_response_keys (list): list of inference response keys

        Raises:
            ValueError: if one or more of inference_response_keys are unsupported by the model
        """
        if not inference_response_keys:
            return

        cls._check_inference_keys(inference_response_keys, inference_containers)

        previous_container_output = None

        for container in inference_containers:
            supported_inference_keys_container = cls._get_supported_inference_keys(
                container, default=[]
            )
            if not supported_inference_keys_container:
                previous_container_output = None
                continue
            current_container_output = None
            for key in inference_response_keys:
                if key in supported_inference_keys_container:
                    current_container_output = (
                        current_container_output + "," + key if current_container_output else key
                    )

            if previous_container_output:
                container["Environment"].update(
                    {"SAGEMAKER_INFERENCE_INPUT": previous_container_output}
                )
            if current_container_output:
                container["Environment"].update(
                    {"SAGEMAKER_INFERENCE_OUTPUT": current_container_output}
                )
            previous_container_output = current_container_output
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