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 histogram to summary.
  
  Args:
      tag (str): Data identifier
      values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram
      global_step (int): Global step value to record
      bins (str): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
        other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
      walltime (float): Optional override default walltime (time.time())
        seconds after epoch of event
  
  Examples::
  
      from torch.utils.tensorboard import SummaryWriter
      import numpy as np
      writer = SummaryWriter()
      for i in range(10):
          x = np.random.random(1000)
          writer.add_histogram('distribution centers', x + i, i)
      writer.close()
  
  Expected result:
  
  .. image:: _static/img/tensorboard/add_histogram.png
     :scale: 50 %
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code: def add_histogram(
      self,
      tag,
      values,
      global_step=None,
      bins="tensorflow",
      walltime=None,
      max_bins=None,
  ):
      """Add histogram to summary.
  
      Args:
          tag (str): Data identifier
          values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram
          global_step (int): Global step value to record
          bins (str): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
            other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
          walltime (float): Optional override default walltime (time.time())
            seconds after epoch of event
  
      Examples::
  
          from torch.utils.tensorboard import SummaryWriter
          import numpy as np
          writer = SummaryWriter()
          for i in range(10):
              x = np.random.random(1000)
              writer.add_histogram('distribution centers', x + i, i)
          writer.close()
  
      Expected result:
  
      .. image:: _static/img/tensorboard/add_histogram.png
         :scale: 50 %
  
      """
      torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
      if self._check_caffe2_blob(values):
          from caffe2.python import workspace
  
          values = workspace.FetchBlob(values)
      if isinstance(bins, str) and bins == "tensorflow":
          bins = self.default_bins
      self._get_file_writer().add_summary(
          histogram(tag, values, bins, max_bins=max_bins),
          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 9 for calling the SummaryWriter obj's underlying member method code with example usage: obj.add_histogram(tag, values, global_step, bins, walltime, max_bins) without return types