code: def add_pr_curve(self, tag, labels, predictions, num_thresholds,
                       global_step=None, weights=None):
          
          if num_thresholds < 2:
              raise ValueError('num_thresholds must be >= 2')
          labels = _make_numpy_array(labels)
          predictions = _make_numpy_array(predictions)
          self._file_writer.add_summary(pr_curve_summary(tag, labels, predictions,
                                                         num_thresholds, weights), global_step)
docs: Adds precision-recall curve.
  
          Note: This function internally calls `asnumpy()` for MXNet `NDArray` inputs.
          Since `asnumpy()` is a blocking function call, this function would block the main
          thread till it returns. It may consequently affect the performance of async execution
          of the MXNet engine.
  
          Parameters
          ----------
              tag : str
                  A tag attached to the summary. Used by TensorBoard for organization.
              labels : MXNet `NDArray` or `numpy.ndarray`.
                  The ground truth values. A tensor of 0/1 values with arbitrary shape.
              predictions : MXNet `NDArray` or `numpy.ndarray`.
                  A float32 tensor whose values are in the range `[0, 1]`. Dimensions must match
                  those of `labels`.
              num_thresholds : int
                  Number of thresholds, evenly distributed in `[0, 1]`, to compute PR metrics for.
                  Should be `>= 2`. This value should be a constant integer value, not a tensor
                  that stores an integer.
                  The thresholds for computing the pr curves are calculated in the following way:
                  `width = 1.0 / (num_thresholds - 1),
                  thresholds = [0.0, 1*width, 2*width, 3*width, ..., 1.0]`.
              global_step : int
                  Global step value to record.
              weights : MXNet `NDArray` or `numpy.ndarray`.
                  Optional float32 tensor. Individual counts are multiplied by this value.
                  This tensor must be either the same shape as or broadcastable to the `labels`
                  tensor.