class_docstr: Enable visualizations for TensorBoard.
  
  TensorBoard is a visualization tool provided with TensorFlow. A TensorFlow
  installation is required to use this callback.
  
  This callback logs events for TensorBoard, including:
  
  * Metrics summary plots
  * Training graph visualization
  * Weight histograms
  * Sampled profiling
  
  When used in `model.evaluate()` or regular validation
  in addition to epoch summaries, there will be a summary that records
  evaluation metrics vs `model.optimizer.iterations` written. The metric names
  will be prepended with `evaluation`, with `model.optimizer.iterations` being
  the step in the visualized TensorBoard.
  
  If you have installed TensorFlow with pip, you should be able
  to launch TensorBoard from the command line:
  
  ```
  tensorboard --logdir=path_to_your_logs
  ```
  
  You can find more information about TensorBoard
  [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
  
  Args:
      log_dir: the path of the directory where to save the log files to be
          parsed by TensorBoard. e.g.,
          `log_dir = os.path.join(working_dir, 'logs')`.
          This directory should not be reused by any other callbacks.
      histogram_freq: frequency (in epochs) at which to compute
          weight histograms for the layers of the model. If set to 0,
          histograms won't be computed. Validation data (or split) must be
          specified for histogram visualizations.
      write_graph:  (Not supported at this time)
          Whether to visualize the graph in TensorBoard.
          Note that the log file can become quite large
          when `write_graph` is set to `True`.
      write_images: whether to write model weights to visualize as image in
          TensorBoard.
      write_steps_per_second: whether to log the training steps per second
          into TensorBoard. This supports both epoch and batch frequency
          logging.
      update_freq: `"batch"` or `"epoch"` or integer. When using `"epoch"`,
          writes the losses and metrics to TensorBoard after every epoch.
          If using an integer, let's say `1000`, all metrics and losses
          (including custom ones added by `Model.compile`) will be logged to
          TensorBoard every 1000 batches. `"batch"` is a synonym for 1,
          meaning that they will be written every batch.
          Note however that writing too frequently to TensorBoard can slow
          down your training, especially when used with distribution
          strategies as it will incur additional synchronization overhead.
          Batch-level summary writing is also available via `train_step`
          override. Please see
          [TensorBoard Scalars tutorial](
              https://www.tensorflow.org/tensorboard/scalars_and_keras#batch-level_logging)  # noqa: E501
          for more details.
      profile_batch: (Not supported at this time)
          Profile the batch(es) to sample compute characteristics.
          profile_batch must be a non-negative integer or a tuple of integers.
          A pair of positive integers signify a range of batches to profile.
          By default, profiling is disabled.
      embeddings_freq: frequency (in epochs) at which embedding layers will be
          visualized. If set to 0, embeddings won't be visualized.
      embeddings_metadata: Dictionary which maps embedding layer names to the
          filename of a file in which to save metadata for the embedding layer.
          In case the same metadata file is to be
          used for all embedding layers, a single filename can be passed.
  
  Examples:
  
  Basic usage:
  
  ```python
  tensorboard_callback = keras.callbacks.TensorBoard(log_dir="./logs")
  model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
  # Then run the tensorboard command to view the visualizations.
  ```
  
  Custom batch-level summaries in a subclassed Model:
  
  ```python
  class MyModel(keras.Model):
  
      def build(self, _):
          self.dense = keras.layers.Dense(10)
  
      def call(self, x):
          outputs = self.dense(x)
          tf.summary.histogram('outputs', outputs)
          return outputs
  
  model = MyModel()
  model.compile('sgd', 'mse')
  
  # Make sure to set `update_freq=N` to log a batch-level summary every N
  # batches.  In addition to any `tf.summary` contained in `model.call()`,
  # metrics added in `Model.compile` will be logged every N batches.
  tb_callback = keras.callbacks.TensorBoard('./logs', update_freq=1)
  model.fit(x_train, y_train, callbacks=[tb_callback])
  ```
  
  Custom batch-level summaries in a Functional API Model:
  
  ```python
  def my_summary(x):
      tf.summary.histogram('x', x)
      return x
  
  inputs = keras.Input(10)
  x = keras.layers.Dense(10)(inputs)
  outputs = keras.layers.Lambda(my_summary)(x)
  model = keras.Model(inputs, outputs)
  model.compile('sgd', 'mse')
  
  # Make sure to set `update_freq=N` to log a batch-level summary every N
  # batches. In addition to any `tf.summary` contained in `Model.call`,
  # metrics added in `Model.compile` will be logged every N batches.
  tb_callback = keras.callbacks.TensorBoard('./logs', update_freq=1)
  model.fit(x_train, y_train, callbacks=[tb_callback])
  ```
  
  Profiling:
  
  ```python
  # Profile a single batch, e.g. the 5th batch.
  tensorboard_callback = keras.callbacks.TensorBoard(
      log_dir='./logs', profile_batch=5)
  model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
  
  # Profile a range of batches, e.g. from 10 to 20.
  tensorboard_callback = keras.callbacks.TensorBoard(
      log_dir='./logs', profile_batch=(10,20))
  model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
  ```
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code: def on_train_end(self, logs=None):
      self._pop_writer()
  
      if self._is_tracing:
          self._stop_trace()
  
      self._close_writers()
  
imports: import logging
  import os
  import sys
  import time
  import warnings
  import tree
  from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.callbacks.callback import Callback
  from keras.layers import Embedding
  from keras.optimizers import Optimizer
  from keras.utils import file_utils
label_desc: Use image node_id 14 for calling the TensorBoard obj's underlying member method code with example usage: obj.on_train_end(logs) without return types