class_docstr: An `EventAccumulator` takes an event generator, and accumulates the
  values.
  
  The `EventAccumulator` is intended to provide a convenient Python interface
  for loading Event data written during a TensorFlow run. TensorFlow writes out
  `Event` protobuf objects, which have a timestamp and step number, and often
  contain a `Summary`. Summaries can have different kinds of data like an image,
  a scalar value, or a histogram. The Summaries also have a tag, which we use to
  organize logically related data. The `EventAccumulator` supports retrieving
  the `Event` and `Summary` data by its tag.
  
  Calling `Tags()` gets a map from `tagType` (e.g. `'images'`,
  `'compressedHistograms'`, `'scalars'`, etc) to the associated tags for those
  data types. Then, various functional endpoints (eg
  `Accumulator.Scalars(tag)`) allow for the retrieval of all data
  associated with that tag.
  
  The `Reload()` method synchronously loads all of the data written so far.
  
  Histograms, audio, and images are very large, so storing all of them is not
  recommended.
  
  Fields:
    audios: A reservoir.Reservoir of audio summaries.
    compressed_histograms: A reservoir.Reservoir of compressed
        histogram summaries.
    histograms: A reservoir.Reservoir of histogram summaries.
    images: A reservoir.Reservoir of image summaries.
    most_recent_step: Step of last Event proto added. This should only
        be accessed from the thread that calls Reload. This is -1 if
        nothing has been loaded yet.
    most_recent_wall_time: Timestamp of last Event proto added. This is
        a float containing seconds from the UNIX epoch, or -1 if
        nothing has been loaded yet. This should only be accessed from
        the thread that calls Reload.
    path: A file path to a directory containing tf events files, or a single
        tf events file. The accumulator will load events from this path.
    scalars: A reservoir.Reservoir of scalar summaries.
    tensors: A reservoir.Reservoir of tensor summaries.
  
  @@Tensors
class_docstr_tok: ["An","`","EventAccumulator","`","takes","an","event","generator",",","and","accumulates","the","values",".","The","`","EventAccumulator","`","is","intended","to","provide","a","convenient","Python","interface","for","loading","Event","data","written","during","a","TensorFlow","run",".","TensorFlow","writes","out","`","Event","`","protobuf","objects",",","which","have","a","timestamp","and","step","number",",","and","often","contain","a","`","Summary","`",".","Summaries","can","have","different","kinds","of","data","like","an","image",",","a","scalar","value",",","or","a","histogram",".","The","Summaries","also","have","a","tag",",","which","we","use","to","organize","logically","related","data",".","The","`","EventAccumulator","`","supports","retrieving","the","`","Event","`","and","`","Summary","`","data","by","its","tag",".","Calling","`","Tags","(",")","`","gets","a","map","from","`","tagType","`","(","e.g",".","`","'images","'","`",",","`","'compressedHistograms","'","`",",","`","'scalars","'","`",",","etc",")","to","the","associated","tags","for","those","data","types",".","Then",",","various","functional","endpoints","(","eg","`","Accumulator.Scalars","(","tag",")","`",")","allow","for","the","retrieval","of","all","data","associated","with","that","tag",".","The","`","Reload","(",")","`","method","synchronously","loads","all","of","the","data","written","so","far",".","Histograms",",","audio",",","and","images","are","very","large",",","so","storing","all","of","them","is","not","recommended",".","Fields",":","audios",":","A","reservoir.Reservoir","of","audio","summaries",".","compressed_histograms",":","A","reservoir.Reservoir","of","compressed","histogram","summaries",".","histograms",":","A","reservoir.Reservoir","of","histogram","summaries",".","images",":","A","reservoir.Reservoir","of","image","summaries",".","most_recent_step",":","Step","of","last","Event","proto","added",".","This","should","only","be","accessed","from","the","thread","that","calls","Reload",".","This","is","-1","if","nothing","has","been","loaded","yet",".","most_recent_wall_time",":","Timestamp","of","last","Event","proto","added",".","This","is","a","float","containing","seconds","from","the","UNIX","epoch",",","or","-1","if","nothing","has","been","loaded","yet",".","This","should","only","be","accessed","from","the","thread","that","calls","Reload",".","path",":","A","file","path","to","a","directory","containing","tf","events","files",",","or","a","single","tf","events","file",".","The","accumulator","will","load","events","from","this","path",".","scalars",":","A","reservoir.Reservoir","of","scalar","summaries",".","tensors",":","A","reservoir.Reservoir","of","tensor","summaries",".","@","@","Tensors"]
docstr: Processes an image by adding it to accumulated state.
docstr_tok: ["Processes","an","image","by","adding","it","to","accumulated","state","."]
code: def _ProcessImage(self, tag, wall_time, step, image):
      """Processes an image by adding it to accumulated state."""
      event = ImageEvent(
          wall_time=wall_time,
          step=step,
          encoded_image_string=image.encoded_image_string,
          width=image.width,
          height=image.height,
      )
      self.images.AddItem(tag, event)
  
imports: import collections
  import dataclasses
  import threading
  from typing import Optional, Sequence, Tuple
  from tensorboard.backend.event_processing import directory_watcher
  from tensorboard.backend.event_processing import event_file_loader
  from tensorboard.backend.event_processing import event_util
  from tensorboard.backend.event_processing import io_wrapper
  from tensorboard.backend.event_processing import plugin_asset_util
  from tensorboard.backend.event_processing import reservoir
  from tensorboard.backend.event_processing import tag_types
  from tensorboard.compat.proto import config_pb2
  from tensorboard.compat.proto import event_pb2
  from tensorboard.compat.proto import graph_pb2
  from tensorboard.compat.proto import meta_graph_pb2
  from tensorboard.compat.proto import tensor_pb2
  from tensorboard.plugins.distribution import compressor
  from tensorboard.util import tb_logging
label_desc: Use image node_id 26 for calling the EventAccumulator obj's underlying member method code with example usage: obj._ProcessImage(tag, wall_time, step, image) without return types