class_docstr: This class implements the RecMetricComputation for Calibration, which is the
  ratio between the prediction and the labels (conversions).
  
  The constructor arguments are defined in RecMetricComputation.
  See the docstring of RecMetricComputation for more detail.
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code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      super().__init__(*args, **kwargs)
      self._add_state(
          CALIBRATION_NUM,
          torch.zeros(self._n_tasks, dtype=torch.double),
          add_window_state=True,
          dist_reduce_fx="sum",
          persistent=True,
      )
      self._add_state(
          CALIBRATION_DENOM,
          torch.zeros(self._n_tasks, dtype=torch.double),
          add_window_state=True,
          dist_reduce_fx="sum",
          persistent=True,
      )
  
file_path: torchrec/torchrec/metrics/calibration.py
imports: from typing import Any, cast, Dict, List, Optional, Type
  import torch
  from torchrec.metrics.metrics_namespace import MetricName, MetricNamespace, MetricPrefix
  from torchrec.metrics.rec_metric import MetricComputationReport, RecMetric, RecMetricComputation, RecMetricException
label_desc: Use image node_id 1 to create a new CalibrationMetricComputation object from inherited base classes: RecMetricComputation with example: obj = CalibrationMetricComputation()