docs: def pipeline_container_def(models, instance_type=None):
      """
      Create a definition for executing a pipeline of containers as part of a SageMaker model.
      Args:
          models (list[sagemaker.Model]): this will be a list of ``sagemaker.Model`` objects in the order the inference
          should be invoked.
          instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge' (default: None).
      Returns:
          list[dict[str, str]]: list of container definition objects usable with with the CreateModel API for inference
          pipelines if passed via `Containers` field.
      """
      c_defs = []  # should contain list of container definitions in the same order customer passed
      for model in models:
          c_defs.append(model.prepare_container_def(instance_type))
      return c_defs