class_docstr: Accepts model metrics parameters for conversion to request dict.
class_docstr_tok: ["Accepts","model","metrics","parameters","for","conversion","to","request","dict","."]
docstr: Initialize a ``ModelMetrics`` instance and turn parameters into dict.
  
  Args:
      model_statistics (MetricsSource): A metric source object that represents
          model statistics (default: None).
      model_constraints (MetricsSource): A metric source object that represents
          model constraints (default: None).
      model_data_statistics (MetricsSource): A metric source object that represents
          model data statistics (default: None).
      model_data_constraints (MetricsSource): A metric source object that represents
          model data constraints (default: None).
      bias (MetricsSource): A metric source object that represents bias report
          (default: None).
      explainability (MetricsSource): A metric source object that represents
          explainability report (default: None).
      bias_pre_training (MetricsSource): A metric source object that represents
          Pre-training report (default: None).
      bias_post_training (MetricsSource): A metric source object that represents
          Post-training report (default: None).
docstr_tok: ["Initialize","a","``","ModelMetrics","``","instance","and","turn","parameters","into","dict",".","Args",":","model_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","statistics","(","default",":","None",")",".","model_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","constraints","(","default",":","None",")",".","model_data_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","statistics","(","default",":","None",")",".","model_data_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","constraints","(","default",":","None",")",".","bias","(","MetricsSource",")",":","A","metric","source","object","that","represents","bias","report","(","default",":","None",")",".","explainability","(","MetricsSource",")",":","A","metric","source","object","that","represents","explainability","report","(","default",":","None",")",".","bias_pre_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Pre-training","report","(","default",":","None",")",".","bias_post_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Post-training","report","(","default",":","None",")","."]
code: def __init__(
      self,
      model_statistics: Optional["MetricsSource"] = None,
      model_constraints: Optional["MetricsSource"] = None,
      model_data_statistics: Optional["MetricsSource"] = None,
      model_data_constraints: Optional["MetricsSource"] = None,
      bias: Optional["MetricsSource"] = None,
      explainability: Optional["MetricsSource"] = None,
      bias_pre_training: Optional["MetricsSource"] = None,
      bias_post_training: Optional["MetricsSource"] = None,
  ):
      """Initialize a ``ModelMetrics`` instance and turn parameters into dict.
  
      Args:
          model_statistics (MetricsSource): A metric source object that represents
              model statistics (default: None).
          model_constraints (MetricsSource): A metric source object that represents
              model constraints (default: None).
          model_data_statistics (MetricsSource): A metric source object that represents
              model data statistics (default: None).
          model_data_constraints (MetricsSource): A metric source object that represents
              model data constraints (default: None).
          bias (MetricsSource): A metric source object that represents bias report
              (default: None).
          explainability (MetricsSource): A metric source object that represents
              explainability report (default: None).
          bias_pre_training (MetricsSource): A metric source object that represents
              Pre-training report (default: None).
          bias_post_training (MetricsSource): A metric source object that represents
              Post-training report (default: None).
      """
      self.model_statistics = model_statistics
      self.model_constraints = model_constraints
      self.model_data_statistics = model_data_statistics
      self.model_data_constraints = model_data_constraints
      self.bias = bias
      self.bias_pre_training = bias_pre_training
      self.bias_post_training = bias_post_training
      self.explainability = explainability
  
file_path: sagemaker-python-sdk/src/sagemaker/model_metrics.py
imports: from __future__ import absolute_import
  from typing import Optional, Union
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new ModelMetrics object from inherited base classes: object with example: obj = ModelMetrics(model_statistics, model_constraints, model_data_statistics, model_data_constraints, bias, explainability, bias_pre_training, bias_post_training)