class_docstr: Configuration for SHAP analysis using SageMaker Clarify Explainer.
docstr: Initialize a config object for SHAP analysis.
  
  Args:
      shap_baseline_config (:class:`~sagemaker.explainer.clarify_explainer_config.ClarifyShapBaselineConfig`):
          The configuration for the SHAP baseline of the Kernal SHAP algorithm.
      number_of_samples (int): Optional. The number of samples to be used for analysis by the
          Kernal SHAP algorithm. The number of samples determines the size of the synthetic
          dataset, which has an impact on latency of explainability requests. For more
          information, see the `Synthetic data` of `Configure and create an endpoint
          <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html>`__.
      seed (int): Optional. The starting value used to initialize the random number generator
          in the explainer. Provide a value for this parameter to obtain a deterministic SHAP
          result.
      use_logit (bool): Optional. A Boolean toggle to indicate if you want to use the logit
          function (true) or log-odds units (false) for model predictions. (Default: false)
      text_config (:class:`~sagemaker.explainer.clarify_explainer_config.ClarifyTextConfig`):
          Optional. A parameter that indicates if text features are treated as text and
          explanations are provided for individual units of text. Required for NLP
          explainability only.
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code: def __init__(
      self,
      shap_baseline_config: ClarifyShapBaselineConfig,
      number_of_samples: Optional[int] = None,
      seed: Optional[int] = None,
      use_logit: Optional[bool] = False,
      text_config: Optional[ClarifyTextConfig] = None,
  ):
      """Initialize a config object for SHAP analysis.
  
      Args:
          shap_baseline_config (:class:`~sagemaker.explainer.clarify_explainer_config.ClarifyShapBaselineConfig`):
              The configuration for the SHAP baseline of the Kernal SHAP algorithm.
          number_of_samples (int): Optional. The number of samples to be used for analysis by the
              Kernal SHAP algorithm. The number of samples determines the size of the synthetic
              dataset, which has an impact on latency of explainability requests. For more
              information, see the `Synthetic data` of `Configure and create an endpoint
              <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html>`__.
          seed (int): Optional. The starting value used to initialize the random number generator
              in the explainer. Provide a value for this parameter to obtain a deterministic SHAP
              result.
          use_logit (bool): Optional. A Boolean toggle to indicate if you want to use the logit
              function (true) or log-odds units (false) for model predictions. (Default: false)
          text_config (:class:`~sagemaker.explainer.clarify_explainer_config.ClarifyTextConfig`):
              Optional. A parameter that indicates if text features are treated as text and
              explanations are provided for individual units of text. Required for NLP
              explainability only.
      """  # noqa E501  # pylint: disable=line-too-long
      self.number_of_samples = number_of_samples
      self.seed = seed
      self.shap_baseline_config = shap_baseline_config
      self.text_config = text_config
      self.use_logit = use_logit
  
imports: from __future__ import print_function, absolute_import
  from typing import List, Optional
label_desc: Use image node_id 1 to create a new ClarifyShapConfig object from inherited base classes: object with example: obj = ClarifyShapConfig(shap_baseline_config, number_of_samples, seed, use_logit, text_config)