src/sagemaker/chainer/model.py [256:327]:
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            content_types,
            response_types,
            inference_instances,
            transform_instances,
            model_package_name,
            model_package_group_name,
            image_uri,
            model_metrics,
            metadata_properties,
            marketplace_cert,
            approval_status,
            description,
            drift_check_baselines=drift_check_baselines,
            customer_metadata_properties=customer_metadata_properties,
            domain=domain,
            sample_payload_url=sample_payload_url,
            task=task,
            framework=framework,
            framework_version=framework_version or self.framework_version,
            nearest_model_name=nearest_model_name,
            data_input_configuration=data_input_configuration,
            skip_model_validation=skip_model_validation,
            source_uri=source_uri,
            model_card=model_card,
            model_life_cycle=model_life_cycle,
        )

    def prepare_container_def(
        self,
        instance_type=None,
        accelerator_type=None,
        serverless_inference_config=None,
        accept_eula=None,
        model_reference_arn=None,
    ):
        """Return a container definition with framework configuration set in model environment.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to.
                For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to
                deploy to the instance for loading and making inferences to the
                model. For example, 'ml.eia1.medium'.
            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
                Specifies configuration related to serverless endpoint. Instance type is
                not provided in serverless inference. So this is used to find image URIs.
            accept_eula (bool): For models that require a Model Access Config, specify True or
                False to indicate whether model terms of use have been accepted.
                The `accept_eula` value must be explicitly defined as `True` in order to
                accept the end-user license agreement (EULA) that some
                models require. (Default: None).

        Returns:
            dict[str, str]: A container definition object usable with the
            CreateModel API.
        """
        deploy_image = self.image_uri
        if not deploy_image:
            if instance_type is None and serverless_inference_config is None:
                raise ValueError(
                    "Must supply either an instance type (for choosing CPU vs GPU) or an image URI."
                )

            region_name = self.sagemaker_session.boto_session.region_name
            deploy_image = self.serving_image_uri(
                region_name,
                instance_type,
                accelerator_type=accelerator_type,
                serverless_inference_config=serverless_inference_config,
            )

        deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
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src/sagemaker/mxnet/model.py [258:331]:
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            content_types,
            response_types,
            inference_instances,
            transform_instances,
            model_package_name,
            model_package_group_name,
            image_uri,
            model_metrics,
            metadata_properties,
            marketplace_cert,
            approval_status,
            description,
            drift_check_baselines=drift_check_baselines,
            customer_metadata_properties=customer_metadata_properties,
            domain=domain,
            sample_payload_url=sample_payload_url,
            task=task,
            framework=framework,
            framework_version=framework_version or self.framework_version,
            nearest_model_name=nearest_model_name,
            data_input_configuration=data_input_configuration,
            skip_model_validation=skip_model_validation,
            source_uri=source_uri,
            model_card=model_card,
            model_life_cycle=model_life_cycle,
        )

    def prepare_container_def(
        self,
        instance_type=None,
        accelerator_type=None,
        serverless_inference_config=None,
        accept_eula=None,
        model_reference_arn=None,
    ):
        """Return a container definition with framework configuration.

        Framework configuration is set in model environment variables.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to.
                For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to
                deploy to the instance for loading and making inferences to the
                model. For example, 'ml.eia1.medium'.
            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
                Specifies configuration related to serverless endpoint. Instance type is
                not provided in serverless inference. So this is used to find image URIs.
            accept_eula (bool): For models that require a Model Access Config, specify True or
                False to indicate whether model terms of use have been accepted.
                The `accept_eula` value must be explicitly defined as `True` in order to
                accept the end-user license agreement (EULA) that some
                models require. (Default: None).

        Returns:
            dict[str, str]: A container definition object usable with the
            CreateModel API.
        """
        deploy_image = self.image_uri
        if not deploy_image:
            if instance_type is None and serverless_inference_config is None:
                raise ValueError(
                    "Must supply either an instance type (for choosing CPU vs GPU) or an image URI."
                )

            region_name = self.sagemaker_session.boto_session.region_name
            deploy_image = self.serving_image_uri(
                region_name,
                instance_type,
                accelerator_type=accelerator_type,
                serverless_inference_config=serverless_inference_config,
            )

        deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
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