class_docstr: The objective for optimization during tuning.
  
  Args:
      name: String. The name of the objective.
      direction: String. The value should be "min" or "max" indicating
          whether the objective value should be minimized or maximized.
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docstr: Check if objective value exists in logs.
  
  Args:
      logs: A dictionary with the metric names as the keys and the metric
          values as the values, which is in the same format as the `logs`
          argument for `Callback.on_epoch_end()`.
  
  Returns:
      Boolean, whether we can compute objective value from the logs.
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code: def has_value(self, logs):
      """Check if objective value exists in logs.
  
      Args:
          logs: A dictionary with the metric names as the keys and the metric
              values as the values, which is in the same format as the `logs`
              argument for `Callback.on_epoch_end()`.
  
      Returns:
          Boolean, whether we can compute objective value from the logs.
      """
      return self.name in logs
  
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.engine import metrics_tracking
label_desc: Use image node_id 2 for calling the Objective obj's underlying member method code with example usage: obj.has_value(logs) and returns: unknown