google_cloud_automlops/orchestration/base.py [228:255]:
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    def build(self,
              pipeline_params: dict,
              custom_training_job_specs: Optional[List] = None):
        """Instantiates an abstract built method to create and write pipeline files. Also reads in
        defaults file to save default arguments to attributes.

        Files created must include:
            1. README.md
            2. Dockerfile
            3. Requirements.txt

        Args:
            custom_training_job_specs (dict): Specifies the specs to run the training job with.
            pipeline_params (Optional[List]): Dictionary containing runtime pipeline parameters.
                Defaults to None.

        Raises:
            NotImplementedError: The subclass has not defined the `build` method.
        """
        # Save parameters as attributes
        self.custom_training_job_specs = custom_training_job_specs
        self.pipeline_params = pipeline_params

        # Extract additional attributes from defaults file
        defaults = read_yaml_file(GENERATED_DEFAULTS_FILE)
        self.project_id = defaults['gcp']['project_id']
        self.gs_pipeline_job_spec_path = defaults['pipelines']['gs_pipeline_job_spec_path']
        self.base_image = defaults['gcp']['base_image']
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google_cloud_automlops/orchestration/kfp.py [166:203]:
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    def build(self,
              pipeline_params: dict,
              custom_training_job_specs: Optional[List] = None):
        """Constructs files for running and managing Kubeflow pipelines.

            Files created under AutoMLOps/:
                README.md
                scripts/
                    pipeline_spec/.gitkeep
                    build_components.sh
                    build_pipeline_spec.sh
                    run_pipeline.sh
                    publish_to_topic.sh
                    run_all.sh
                components/
                    component_base/Dockerfile
                    component_base/requirements.txt
                pipelines/
                    pipeline.py
                    pipeline_runner.py
                    requirements.txt
                    runtime_parameters/pipeline_parameter_values.json

        Args:
            custom_training_job_specs (dict): Specifies the specs to run the training job with.
            pipeline_params (Optional[List]): Dictionary containing runtime pipeline parameters. Defaults
                to None.

        """
        # Save parameters as attributes
        self.custom_training_job_specs = custom_training_job_specs
        self.pipeline_params = pipeline_params

        # Extract additional attributes from defaults file
        defaults = read_yaml_file(GENERATED_DEFAULTS_FILE)
        self.project_id = defaults['gcp']['project_id']
        self.gs_pipeline_job_spec_path = defaults['pipelines']['gs_pipeline_job_spec_path']
        self.base_image = defaults['gcp']['base_image']
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