docstr: Adds precision recall curve with raw data.
  
  Args:
      tag (string): Data identifier
      true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts
      false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts
      true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts
      false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts
      precision (torch.Tensor, numpy.array, or string/blobname): precision
      recall (torch.Tensor, numpy.array, or string/blobname): recall
      global_step (int): Global step value to record
      num_thresholds (int): Number of thresholds used to draw the curve.
      see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
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code: def add_pr_curve_raw(
      self,
      tag,
      true_positive_counts,
      false_positive_counts,
      true_negative_counts,
      false_negative_counts,
      precision,
      recall,
      global_step=None,
      num_thresholds=127,
      weights=None,
  ):
      """Adds precision recall curve with raw data.
  
      Args:
          tag (string): Data identifier
          true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts
          false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts
          true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts
          false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts
          precision (torch.Tensor, numpy.array, or string/blobname): precision
          recall (torch.Tensor, numpy.array, or string/blobname): recall
          global_step (int): Global step value to record
          num_thresholds (int): Number of thresholds used to draw the curve.
          see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
      """
      precision, recall = make_np(precision), make_np(recall)
      self.vis.line(
          X=recall,
          Y=precision,
          name=tag,
          opts={
              "title": "PR Curve for {}".format(tag),
              "xlabel": "recall",
              "ylabel": "precision",
          },
      )
  
imports: import gc
  import numpy
  import math
  import json
  import time
  from .summary import compute_curve
  from .utils import figure_to_image
  from .x2num import make_np
label_desc: Use image node_id 16 for calling the VisdomWriter obj's underlying member method code with example usage: obj.add_pr_curve_raw(tag, true_positive_counts, false_positive_counts, true_negative_counts, false_negative_counts, precision, recall, global_step, num_thresholds, weights) without return types