class_docstr: Base metric parser for model monitor model quality
class_docstr_tok: ["Base","metric","parser","for","model","monitor","model","quality"]
docstr: Parse the basic metric in the model monitor model quality.
  
      e.g. {
          "standard_deviation": 0.1,
          "value": 1
      }
  
  Args:
      metric_name (str): metric name.
      metric_data (dict): metric data.
docstr_tok: ["Parse","the","basic","metric","in","the","model","monitor","model","quality",".","e.g",".","{","``","standard_deviation","''",":","0.1",",","``","value","''",":","1","}","Args",":","metric_name","(","str",")",":","metric","name",".","metric_data","(","dict",")",":","metric","data","."]
code: def _parse_basic_metric(self, metric_name: str, metric_data: dict):
      """Parse the basic metric in the model monitor model quality.
  
          e.g. {
              "standard_deviation": 0.1,
              "value": 1
          }
  
      Args:
          metric_name (str): metric name.
          metric_data (dict): metric data.
      """
      metrics = [
          {
              "name": metric_name,
              "value": metric_data["value"],
              "type": "number",
          }
      ]
      if (
          type(metric_data["standard_deviation"])
          in METRIC_VALUE_TYPE_MAP[MetricTypeEnum.NUMBER]
      ):
          metrics.append(
              {
                  "name": f"{metric_name} - standard_deviation",
                  "value": metric_data["standard_deviation"],
                  "type": "number",
              }
          )
      return metrics
  
imports: from __future__ import absolute_import
  from abc import ABC, abstractmethod
  from enum import Enum
  from collections import defaultdict
  from typing import Union, List
  from sagemaker.model_card.schema_constraints import PYTHON_TYPE_TO_METRIC_VALUE_TYPE, METRIC_VALUE_TYPE_MAP, MetricTypeEnum
label_desc: Use image node_id 1 for calling the ModelMonitorModelQualityParserBase obj's underlying member method code with example usage: obj._parse_basic_metric(metric_name, metric_data) and returns: metrics