class_docstr: Writes entries directly to event files in the log_dir to be consumed by TensorBoard.
  
  The `SummaryWriter` class provides a high-level API to create an event file
  in a given directory and add summaries and events to it. The class updates the
  file contents asynchronously. This allows a training program to call methods
  to add data to the file directly from the training loop, without slowing down
  training.
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docstr: Add precision recall curve with raw data.
  
  Args:
      tag (str): Data identifier
      true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts
      false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts
      true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts
      false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts
      precision (torch.Tensor, numpy.ndarray, or string/blobname): precision
      recall (torch.Tensor, numpy.ndarray, or string/blobname): recall
      global_step (int): Global step value to record
      num_thresholds (int): Number of thresholds used to draw the curve.
      walltime (float): Optional override default walltime (time.time())
        seconds after epoch of event
      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,
      walltime=None,
  ):
      """Add precision recall curve with raw data.
  
      Args:
          tag (str): Data identifier
          true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts
          false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts
          true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts
          false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts
          precision (torch.Tensor, numpy.ndarray, or string/blobname): precision
          recall (torch.Tensor, numpy.ndarray, or string/blobname): recall
          global_step (int): Global step value to record
          num_thresholds (int): Number of thresholds used to draw the curve.
          walltime (float): Optional override default walltime (time.time())
            seconds after epoch of event
          see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
      """
      torch._C._log_api_usage_once(
          "tensorboard.logging.add_pr_curve_raw"
      )
      self._get_file_writer().add_summary(
          pr_curve_raw(
              tag,
              true_positive_counts,
              false_positive_counts,
              true_negative_counts,
              false_negative_counts,
              precision,
              recall,
              num_thresholds,
              weights,
          ),
          global_step,
          walltime,
      )
  
imports: import os
  import time
  from typing import List, Optional, Union, TYPE_CHECKING
  import torch
  from tensorboard.compat import tf
  from tensorboard.compat.proto import event_pb2
  from tensorboard.compat.proto.event_pb2 import Event, SessionLog
  from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
  from tensorboard.summary.writer.event_file_writer import EventFileWriter
  from ._convert_np import make_np
  from ._embedding import get_embedding_info, make_mat, make_sprite, make_tsv, write_pbtxt
  from ._onnx_graph import load_onnx_graph
  from ._pytorch_graph import graph
  from ._utils import figure_to_image
  from .summary import audio, custom_scalars, histogram, histogram_raw, hparams, image, image_boxes, mesh, pr_curve, pr_curve_raw, scalar, tensor_proto, text, video
label_desc: Use image node_id 23 for calling the SummaryWriter 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, walltime) without return types