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 scalar data to summary.
  
  Args:
      tag (str): Data identifier
      scalar_value (float or string/blobname): Value to save
      global_step (int): Global step value to record
      walltime (float): Optional override default walltime (time.time())
        with seconds after epoch of event
      new_style (boolean): Whether to use new style (tensor field) or old
        style (simple_value field). New style could lead to faster data loading.
  Examples::
  
      from torch.utils.tensorboard import SummaryWriter
      writer = SummaryWriter()
      x = range(100)
      for i in x:
          writer.add_scalar('y=2x', i * 2, i)
      writer.close()
  
  Expected result:
  
  .. image:: _static/img/tensorboard/add_scalar.png
     :scale: 50 %
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code: def add_scalar(
      self,
      tag,
      scalar_value,
      global_step=None,
      walltime=None,
      new_style=False,
      double_precision=False,
  ):
      """Add scalar data to summary.
  
      Args:
          tag (str): Data identifier
          scalar_value (float or string/blobname): Value to save
          global_step (int): Global step value to record
          walltime (float): Optional override default walltime (time.time())
            with seconds after epoch of event
          new_style (boolean): Whether to use new style (tensor field) or old
            style (simple_value field). New style could lead to faster data loading.
      Examples::
  
          from torch.utils.tensorboard import SummaryWriter
          writer = SummaryWriter()
          x = range(100)
          for i in x:
              writer.add_scalar('y=2x', i * 2, i)
          writer.close()
  
      Expected result:
  
      .. image:: _static/img/tensorboard/add_scalar.png
         :scale: 50 %
  
      """
      torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
      if self._check_caffe2_blob(scalar_value):
          from caffe2.python import workspace
  
          scalar_value = workspace.FetchBlob(scalar_value)
  
      summary = scalar(
          tag,
          scalar_value,
          new_style=new_style,
          double_precision=double_precision,
      )
      self._get_file_writer().add_summary(
          summary, 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 6 for calling the SummaryWriter obj's underlying member method code with example usage: obj.add_scalar(tag, scalar_value, global_step, walltime, new_style, double_precision) without return types