class_docstr: Configuration builder for WitWidget settings.
docstr: Sets the model information for a model served by AI Platform.
  
  AI Platform Prediction a Google Cloud serving platform.
  
  Args:
    project: The name of the AI Platform Prediction project.
    model: The name of the AI Platform Prediction model.
    version: Optional, the version of the AI Platform Prediction model.
    force_json_input: Optional. If True and examples are provided as
    tf.Example protos, convert them to raw JSON objects before sending them
    for inference to this model.
    adjust_prediction: Optional. If not None then this function takes the
    prediction output from the model for a single example and converts it to
    the appropriate format - a regression score or a list of class scores. Only
    necessary if the model doesn't already abide by this format.
    adjust_example: Optional. If not None then this function takes an example
    to run prediction on and converts it to the format expected by the model.
    Necessary for example if the served model expects a single data value to
    run inference on instead of a list or dict of values.
    adjust_attribution: Optional. If not None and the model returns attribution
    information, then this function takes the attribution information for an
    example and converts it to the format expected by the tool, which is a
    dictionary of input feature names to attribution scores. Usually necessary
    if making use of adjust_example and the model returns attribution results.
    service_name: Optional. Name of the AI Platform Prediction service. Defaults
    to 'ml'.
    service_version: Optional. Version of the AI Platform Prediction service. Defaults
    to 'v1'.
    get_explanations: Optional. If a model is deployed with explanations,
    then this specifies if explanations will be calculated and displayed.
    Defaults to True.
    batch_size: Optional. Sets the individual batch size to send for
    prediction. Defaults to 500.
    api_key: Optional. A generated API key to send with the requests to AI
    Platform.
  
  Returns:
    self, in order to enabled method chaining.
docstr_tok: ["Sets","the","model","information","for","a","model","served","by","AI","Platform",".","AI","Platform","Prediction","a","Google","Cloud","serving","platform",".","Args",":","project",":","The","name","of","the","AI","Platform","Prediction","project",".","model",":","The","name","of","the","AI","Platform","Prediction","model",".","version",":","Optional",",","the","version","of","the","AI","Platform","Prediction","model",".","force_json_input",":","Optional",".","If","True","and","examples","are","provided","as","tf.Example","protos",",","convert","them","to","raw","JSON","objects","before","sending","them","for","inference","to","this","model",".","adjust_prediction",":","Optional",".","If","not","None","then","this","function","takes","the","prediction","output","from","the","model","for","a","single","example","and","converts","it","to","the","appropriate","format","-","a","regression","score","or","a","list","of","class","scores",".","Only","necessary","if","the","model","does","n't","already","abide","by","this","format",".","adjust_example",":","Optional",".","If","not","None","then","this","function","takes","an","example","to","run","prediction","on","and","converts","it","to","the","format","expected","by","the","model",".","Necessary","for","example","if","the","served","model","expects","a","single","data","value","to","run","inference","on","instead","of","a","list","or","dict","of","values",".","adjust_attribution",":","Optional",".","If","not","None","and","the","model","returns","attribution","information",",","then","this","function","takes","the","attribution","information","for","an","example","and","converts","it","to","the","format","expected","by","the","tool",",","which","is","a","dictionary","of","input","feature","names","to","attribution","scores",".","Usually","necessary","if","making","use","of","adjust_example","and","the","model","returns","attribution","results",".","service_name",":","Optional",".","Name","of","the","AI","Platform","Prediction","service",".","Defaults","to","'ml","'",".","service_version",":","Optional",".","Version","of","the","AI","Platform","Prediction","service",".","Defaults","to","'v1","'",".","get_explanations",":","Optional",".","If","a","model","is","deployed","with","explanations",",","then","this","specifies","if","explanations","will","be","calculated","and","displayed",".","Defaults","to","True",".","batch_size",":","Optional",".","Sets","the","individual","batch","size","to","send","for","prediction",".","Defaults","to","500.","api_key",":","Optional",".","A","generated","API","key","to","send","with","the","requests","to","AI","Platform",".","Returns",":","self",",","in","order","to","enabled","method","chaining","."]
code: def set_ai_platform_model(
      self,
      project,
      model,
      version=None,
      force_json_input=None,
      adjust_prediction=None,
      adjust_example=None,
      adjust_attribution=None,
      service_name="ml",
      service_version="v1",
      get_explanations=True,
      batch_size=500,
      api_key=None,
  ):
      """Sets the model information for a model served by AI Platform.
  
      AI Platform Prediction a Google Cloud serving platform.
  
      Args:
        project: The name of the AI Platform Prediction project.
        model: The name of the AI Platform Prediction model.
        version: Optional, the version of the AI Platform Prediction model.
        force_json_input: Optional. If True and examples are provided as
        tf.Example protos, convert them to raw JSON objects before sending them
        for inference to this model.
        adjust_prediction: Optional. If not None then this function takes the
        prediction output from the model for a single example and converts it to
        the appropriate format - a regression score or a list of class scores. Only
        necessary if the model doesn't already abide by this format.
        adjust_example: Optional. If not None then this function takes an example
        to run prediction on and converts it to the format expected by the model.
        Necessary for example if the served model expects a single data value to
        run inference on instead of a list or dict of values.
        adjust_attribution: Optional. If not None and the model returns attribution
        information, then this function takes the attribution information for an
        example and converts it to the format expected by the tool, which is a
        dictionary of input feature names to attribution scores. Usually necessary
        if making use of adjust_example and the model returns attribution results.
        service_name: Optional. Name of the AI Platform Prediction service. Defaults
        to 'ml'.
        service_version: Optional. Version of the AI Platform Prediction service. Defaults
        to 'v1'.
        get_explanations: Optional. If a model is deployed with explanations,
        then this specifies if explanations will be calculated and displayed.
        Defaults to True.
        batch_size: Optional. Sets the individual batch size to send for
        prediction. Defaults to 500.
        api_key: Optional. A generated API key to send with the requests to AI
        Platform.
  
      Returns:
        self, in order to enabled method chaining.
      """
      self.set_inference_address(project)
      self.set_model_name(model)
      self.store("use_aip", True)
      self.store("aip_service_name", service_name)
      self.store("aip_service_version", service_version)
      self.store("aip_batch_size", batch_size)
      self.store("get_explanations", get_explanations)
      if version is not None:
          self.set_model_signature(version)
      if force_json_input:
          self.store("force_json_input", True)
      if adjust_prediction:
          self.store("adjust_prediction", adjust_prediction)
      if adjust_example:
          self.store("adjust_example", adjust_example)
      if adjust_attribution:
          self.store("adjust_attribution", adjust_attribution)
      if api_key:
          self.store("aip_api_key", api_key)
      return self
  
imports: import sys
  import tensorflow
label_desc: Use image node_id 28 for calling the WitConfigBuilder obj's underlying member method code with example usage: obj.set_ai_platform_model(project, model, version, force_json_input, adjust_prediction, adjust_example, adjust_attribution, service_name, service_version, get_explanations, batch_size, api_key) and returns: self