class_docstr: Calibration.
  
  Calibration in this context is defined as the total weighted predictions /
  total weighted labels.
class_docstr_tok: ["Calibration",".","Calibration","in","this","context","is","defined","as","the","total","weighted","predictions","\/","total","weighted","labels","."]
docstr: Initializes calibration.
  
  Args:
    name: Metric name.
docstr_tok: ["Initializes","calibration",".","Args",":","name",":","Metric","name","."]
code: def __init__(self, name: str = CALIBRATION_NAME):
      """Initializes calibration.
  
      Args:
        name: Metric name.
      """
      super().__init__(
          metric_util.merge_per_key_computations(_calibration),
          name=name,
      )
  
imports: from typing import Any, Dict, Iterable, Optional
  import apache_beam
  import numpy
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 1 to create a new Calibration object from inherited base classes: metric_types with example: obj = Calibration(name)