class_docstr: Base class for streaming correlation metrics.
  
  Based on https://arxiv.org/abs/1712.01521.
  
  It stores and updates the joint and marginal histograms of (`y_true`, `y_pred`).
  
  The concrete classes estimate the different correlation metrics
  based on those histograms.
class_docstr_tok: ["Base","class","for","streaming","correlation","metrics",".","Based","on","https",":","\/\/arxiv.org\/abs\/1712.01521",".","It","stores","and","updates","the","joint","and","marginal","histograms","of","(","`","y_true","`",",","`","y_pred","`",")",".","The","concrete","classes","estimate","the","different","correlation","metrics","based","on","those","histograms","."]
docstr: Returns the serializable config of the metric.
docstr_tok: ["Returns","the","serializable","config","of","the","metric","."]
code: def get_config(self):
      """Returns the serializable config of the metric."""
  
      config = {
          "actual_min": self.actual_min,
          "actual_max": self.actual_max,
          "preds_min": self.preds_min,
          "preds_max": self.preds_max,
          "actual_cutpoints": self.actual_cutpoints,
          "preds_cutpoints": self.preds_cutpoints,
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
imports: from abc import abstractmethod
  import numpy
  import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.metrics import Metric
  from tensorflow_addons.utils.types import AcceptableDTypes
  from typeguard import typechecked
label_desc: Use image node_id 4 for calling the CorrelationBase obj's underlying member method code with example usage: obj.get_config() and returns: dict