class_docstr: A mixin class for SageMaker ``Inference Recommender`` that will be extended by ``Model``
class_docstr_tok: ["A","mixin","class","for","SageMaker","``","Inference","Recommender","``","that","will","be","extended","by","``","Model","``"]
docstr: Check and update params based on inference recommendation id or right size case
docstr_tok: ["Check","and","update","params","based","on","inference","recommendation","id","or","right","size","case"]
code: def _update_params(
      self,
      **kwargs,
  ):
      """Check and update params based on inference recommendation id or right size case"""
      instance_type = kwargs["instance_type"]
      initial_instance_count = kwargs["initial_instance_count"]
      accelerator_type = kwargs["accelerator_type"]
      async_inference_config = kwargs["async_inference_config"]
      serverless_inference_config = kwargs[
          "serverless_inference_config"
      ]
      explainer_config = kwargs["explainer_config"]
      inference_recommendation_id = kwargs[
          "inference_recommendation_id"
      ]
      inference_recommender_job_results = kwargs[
          "inference_recommender_job_results"
      ]
      if inference_recommendation_id is not None:
          inference_recommendation = self._update_params_for_recommendation_id(
              instance_type=instance_type,
              initial_instance_count=initial_instance_count,
              accelerator_type=accelerator_type,
              async_inference_config=async_inference_config,
              serverless_inference_config=serverless_inference_config,
              inference_recommendation_id=inference_recommendation_id,
              explainer_config=explainer_config,
          )
      elif inference_recommender_job_results is not None:
          inference_recommendation = self._update_params_for_right_size(
              instance_type,
              initial_instance_count,
              accelerator_type,
              serverless_inference_config,
              async_inference_config,
              explainer_config,
          )
  
      return (
          inference_recommendation
          if inference_recommendation
          else (instance_type, initial_instance_count)
      )
  
imports: from __future__ import absolute_import
  import logging
  import re
  from typing import List, Dict, Optional
  import sagemaker
  from sagemaker.parameter import CategoricalParameter
label_desc: Use image node_id 2 for calling the InferenceRecommenderMixin obj's underlying member method code with example usage: obj._update_params() and returns: unknown