class_docstr: The inference configuration parameter for the model container.
docstr: Initialize a config object for model container.
  
  Args:
      feature_headers (list[str]): Optional. The names of the features. If provided, these are
          included in the endpoint response payload to help readability of the
          ``InvokeEndpoint`` output.
      feature_types (list[str]): Optional. A list of data types of the features. Applicable
          only to NLP explainability. If provided, ``feature_types`` must have at least one
          ``'text'`` string (for example, ``['text']``). If ``feature_types`` is not provided,
          the explainer infers the feature types based on the baseline data. The feature
          types are included in the endpoint response payload.
      features_attribute (str): Optional. Provides the JMESPath expression to extract the
          features from a model container input in JSON Lines format. For example,
          if ``features_attribute`` is the JMESPath expression ``'myfeatures'``, it extracts a
          list of features ``[1,2,3]`` from request data ``'{"myfeatures":[1,2,3]}'``.
      probability_index (int): Optional. A zero-based index used to extract a probability
          value (score) or list from model container output in CSV format. If this value is
          not provided, the entire model container output will be treated as a probability
          value (score) or list. See examples `here
          <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ClarifyInferenceConfig.html#sagemaker-Type-ClarifyInferenceConfig-ProbabilityIndex>`__.
      probability_attribute (str): Optional. A JMESPath expression used to extract the
          probability (or score) from the model container output if the model container
          is in JSON Lines format. See examples `here
          <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ClarifyInferenceConfig.html#sagemaker-Type-ClarifyInferenceConfig-ProbabilityAttribute>`__.
      label_index (int): Optional. A zero-based index used to extract a label header or list
          of label headers from model container output in CSV format.
      label_attribute (str): Optional. A JMESPath expression used to locate the list of label
          headers in the model container output.
      label_headers (list[str]): Optional. For multiclass classification problems, the label
          headers are the names of the classes. Otherwise, the label header is the name of
          the predicted label. These are used to help readability for the output of the
          ``InvokeEndpoint`` API.
      max_payload_in_mb (int): Optional. The maximum payload size (MB) allowed of a request
          from the explainer to the model container. (Default: 6)
      max_record_count (int): Optional. The maximum number of records in a request that the
          model container can process when querying the model container for the predictions
          of a `synthetic dataset
          <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-synthetic>`__.
          A record is a unit of input data that inference can be made on, for example, a
          single line in CSV data. If ``max_record_count`` is ``1``, the model container
          expects one record per request. A value of 2 or greater means that the model expects
          batch requests, which can reduce overhead and speed up the inferencing process. If
          this parameter is not provided, the explainer will tune the record count per request
          according to the model container's capacity at runtime.
      content_template (str): Optional. A template string used to format a JSON record into an
          acceptable model container input. For example, a ``ContentTemplate`` string ``'{
          "myfeatures":$features}'`` will format a list of features ``[1,2,3]`` into the
          record string ``'{"myfeatures":[1,2,3]}'``. Required only when the model
          container input is in JSON Lines format.
docstr_tok: ["Initialize","a","config","object","for","model","container",".","Args",":","feature_headers","(","list","[","str","]",")",":","Optional",".","The","names","of","the","features",".","If","provided",",","these","are","included","in","the","endpoint","response","payload","to","help","readability","of","the","``","InvokeEndpoint","``","output",".","feature_types","(","list","[","str","]",")",":","Optional",".","A","list","of","data","types","of","the","features",".","Applicable","only","to","NLP","explainability",".","If","provided",",","``","feature_types","``","must","have","at","least","one","``","'text","'","``","string","(","for","example",",","``","[","'text","'","]","``",")",".","If","``","feature_types","``","is","not","provided",",","the","explainer","infers","the","feature","types","based","on","the","baseline","data",".","The","feature","types","are","included","in","the","endpoint","response","payload",".","features_attribute","(","str",")",":","Optional",".","Provides","the","JMESPath","expression","to","extract","the","features","from","a","model","container","input","in","JSON","Lines","format",".","For","example",",","if","``","features_attribute","``","is","the","JMESPath","expression","``","'myfeatures","'","``",",","it","extracts","a","list","of","features","``","[","1,2,3","]","``","from","request","data","``","'","{","``","myfeatures","''",":","[","1,2,3","]","}","'","``",".","probability_index","(","int",")",":","Optional",".","A","zero-based","index","used","to","extract","a","probability","value","(","score",")","or","list","from","model","container","output","in","CSV","format",".","If","this","value","is","not","provided",",","the","entire","model","container","output","will","be","treated","as","a","probability","value","(","score",")","or","list",".","See","examples","`","here","<","https",":","\/\/docs.aws.amazon.com\/sagemaker\/latest\/APIReference\/API_ClarifyInferenceConfig.html","#","sagemaker-Type-ClarifyInferenceConfig-ProbabilityIndex",">","`","__",".","probability_attribute","(","str",")",":","Optional",".","A","JMESPath","expression","used","to","extract","the","probability","(","or","score",")","from","the","model","container","output","if","the","model","container","is","in","JSON","Lines","format",".","See","examples","`","here","<","https",":","\/\/docs.aws.amazon.com\/sagemaker\/latest\/APIReference\/API_ClarifyInferenceConfig.html","#","sagemaker-Type-ClarifyInferenceConfig-ProbabilityAttribute",">","`","__",".","label_index","(","int",")",":","Optional",".","A","zero-based","index","used","to","extract","a","label","header","or","list","of","label","headers","from","model","container","output","in","CSV","format",".","label_attribute","(","str",")",":","Optional",".","A","JMESPath","expression","used","to","locate","the","list","of","label","headers","in","the","model","container","output",".","label_headers","(","list","[","str","]",")",":","Optional",".","For","multiclass","classification","problems",",","the","label","headers","are","the","names","of","the","classes",".","Otherwise",",","the","label","header","is","the","name","of","the","predicted","label",".","These","are","used","to","help","readability","for","the","output","of","the","``","InvokeEndpoint","``","API",".","max_payload_in_mb","(","int",")",":","Optional",".","The","maximum","payload","size","(","MB",")","allowed","of","a","request","from","the","explainer","to","the","model","container",".","(","Default",":","6",")","max_record_count","(","int",")",":","Optional",".","The","maximum","number","of","records","in","a","request","that","the","model","container","can","process","when","querying","the","model","container","for","the","predictions","of","a","`","synthetic","dataset","<","https",":","\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/clarify-online-explainability-create-endpoint.html","#","clarify-online-explainability-create-endpoint-synthetic",">","`","__",".","A","record","is","a","unit","of","input","data","that","inference","can","be","made","on",",","for","example",",","a","single","line","in","CSV","data",".","If","``","max_record_count","``","is","``","1","``",",","the","model","container","expects","one","record","per","request",".","A","value","of","2","or","greater","means","that","the","model","expects","batch","requests",",","which","can","reduce","overhead","and","speed","up","the","inferencing","process",".","If","this","parameter","is","not","provided",",","the","explainer","will","tune","the","record","count","per","request","according","to","the","model","container","'s","capacity","at","runtime",".","content_template","(","str",")",":","Optional",".","A","template","string","used","to","format","a","JSON","record","into","an","acceptable","model","container","input",".","For","example",",","a","``","ContentTemplate","``","string","``","'","{","``","myfeatures","''",":","$","features","}","'","``","will","format","a","list","of","features","``","[","1,2,3","]","``","into","the","record","string","``","'","{","``","myfeatures","''",":","[","1,2,3","]","}","'","``",".","Required","only","when","the","model","container","input","is","in","JSON","Lines","format","."]
code: def __init__(
      self,
      feature_headers: Optional[List[str]] = None,
      feature_types: Optional[List[str]] = None,
      features_attribute: Optional[str] = None,
      probability_index: Optional[int] = None,
      probability_attribute: Optional[str] = None,
      label_index: Optional[int] = None,
      label_attribute: Optional[str] = None,
      label_headers: Optional[List[str]] = None,
      max_payload_in_mb: Optional[int] = 6,
      max_record_count: Optional[int] = None,
      content_template: Optional[str] = None,
  ):
      """Initialize a config object for model container.
  
      Args:
          feature_headers (list[str]): Optional. The names of the features. If provided, these are
              included in the endpoint response payload to help readability of the
              ``InvokeEndpoint`` output.
          feature_types (list[str]): Optional. A list of data types of the features. Applicable
              only to NLP explainability. If provided, ``feature_types`` must have at least one
              ``'text'`` string (for example, ``['text']``). If ``feature_types`` is not provided,
              the explainer infers the feature types based on the baseline data. The feature
              types are included in the endpoint response payload.
          features_attribute (str): Optional. Provides the JMESPath expression to extract the
              features from a model container input in JSON Lines format. For example,
              if ``features_attribute`` is the JMESPath expression ``'myfeatures'``, it extracts a
              list of features ``[1,2,3]`` from request data ``'{"myfeatures":[1,2,3]}'``.
          probability_index (int): Optional. A zero-based index used to extract a probability
              value (score) or list from model container output in CSV format. If this value is
              not provided, the entire model container output will be treated as a probability
              value (score) or list. See examples `here
              <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ClarifyInferenceConfig.html#sagemaker-Type-ClarifyInferenceConfig-ProbabilityIndex>`__.
          probability_attribute (str): Optional. A JMESPath expression used to extract the
              probability (or score) from the model container output if the model container
              is in JSON Lines format. See examples `here
              <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ClarifyInferenceConfig.html#sagemaker-Type-ClarifyInferenceConfig-ProbabilityAttribute>`__.
          label_index (int): Optional. A zero-based index used to extract a label header or list
              of label headers from model container output in CSV format.
          label_attribute (str): Optional. A JMESPath expression used to locate the list of label
              headers in the model container output.
          label_headers (list[str]): Optional. For multiclass classification problems, the label
              headers are the names of the classes. Otherwise, the label header is the name of
              the predicted label. These are used to help readability for the output of the
              ``InvokeEndpoint`` API.
          max_payload_in_mb (int): Optional. The maximum payload size (MB) allowed of a request
              from the explainer to the model container. (Default: 6)
          max_record_count (int): Optional. The maximum number of records in a request that the
              model container can process when querying the model container for the predictions
              of a `synthetic dataset
              <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-synthetic>`__.
              A record is a unit of input data that inference can be made on, for example, a
              single line in CSV data. If ``max_record_count`` is ``1``, the model container
              expects one record per request. A value of 2 or greater means that the model expects
              batch requests, which can reduce overhead and speed up the inferencing process. If
              this parameter is not provided, the explainer will tune the record count per request
              according to the model container's capacity at runtime.
          content_template (str): Optional. A template string used to format a JSON record into an
              acceptable model container input. For example, a ``ContentTemplate`` string ``'{
              "myfeatures":$features}'`` will format a list of features ``[1,2,3]`` into the
              record string ``'{"myfeatures":[1,2,3]}'``. Required only when the model
              container input is in JSON Lines format.
      """  # noqa E501  # pylint: disable=line-too-long
      self.feature_headers = feature_headers
      self.feature_types = feature_types
      self.features_attribute = features_attribute
      self.probability_index = probability_index
      self.probability_attribute = probability_attribute
      self.label_index = label_index
      self.label_attribute = label_attribute
      self.label_headers = label_headers
      self.max_payload_in_mb = max_payload_in_mb
      self.max_record_count = max_record_count
      self.content_template = content_template
  
imports: from __future__ import print_function, absolute_import
  from typing import List, Optional
label_desc: Use image node_id 1 to create a new ClarifyInferenceConfig object from inherited base classes: object with example: obj = ClarifyInferenceConfig(feature_headers, feature_types, features_attribute, probability_index, probability_attribute, label_index, label_attribute, label_headers, max_payload_in_mb, max_record_count, content_template)