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.
  
  Plotting a precision-recall curve lets you understand your model's
  performance under different threshold settings. With this function,
  you provide the ground truth labeling (T/F) and prediction confidence
  (usually the output of your model) for each target. The TensorBoard UI
  will let you choose the threshold interactively.
  
  Args:
      tag (str): Data identifier
      labels (torch.Tensor, numpy.ndarray, or string/blobname):
        Ground truth data. Binary label for each element.
      predictions (torch.Tensor, numpy.ndarray, or string/blobname):
        The probability that an element be classified as true.
        Value should be in [0, 1]
      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
  
  Examples::
  
      from torch.utils.tensorboard import SummaryWriter
      import numpy as np
      labels = np.random.randint(2, size=100)  # binary label
      predictions = np.random.rand(100)
      writer = SummaryWriter()
      writer.add_pr_curve('pr_curve', labels, predictions, 0)
      writer.close()
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code: def add_pr_curve(
      self,
      tag,
      labels,
      predictions,
      global_step=None,
      num_thresholds=127,
      weights=None,
      walltime=None,
  ):
      """Add precision recall curve.
  
      Plotting a precision-recall curve lets you understand your model's
      performance under different threshold settings. With this function,
      you provide the ground truth labeling (T/F) and prediction confidence
      (usually the output of your model) for each target. The TensorBoard UI
      will let you choose the threshold interactively.
  
      Args:
          tag (str): Data identifier
          labels (torch.Tensor, numpy.ndarray, or string/blobname):
            Ground truth data. Binary label for each element.
          predictions (torch.Tensor, numpy.ndarray, or string/blobname):
            The probability that an element be classified as true.
            Value should be in [0, 1]
          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
  
      Examples::
  
          from torch.utils.tensorboard import SummaryWriter
          import numpy as np
          labels = np.random.randint(2, size=100)  # binary label
          predictions = np.random.rand(100)
          writer = SummaryWriter()
          writer.add_pr_curve('pr_curve', labels, predictions, 0)
          writer.close()
  
      """
      torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve")
      labels, predictions = make_np(labels), make_np(predictions)
      self._get_file_writer().add_summary(
          pr_curve(tag, labels, predictions, 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 22 for calling the SummaryWriter obj's underlying member method code with example usage: obj.add_pr_curve(tag, labels, predictions, global_step, num_thresholds, weights, walltime) without return types