class_docstr: Enable visualizations for TensorBoard.
  
  TensorBoard is a visualization tool provided with TensorFlow.
  
  This callback logs events for TensorBoard, including:
  * Metrics summary plots
  * Training graph visualization
  * Activation histograms
  * Sampled profiling
  
  If you have installed TensorFlow with pip, you should be able
  to launch TensorBoard from the command line:
  
  ```sh
  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.
      histogram_freq: frequency (in epochs) at which to compute activation and
        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: whether to visualize the graph in TensorBoard. The log file
        can become quite large when write_graph is set to True.
      write_grads: whether to visualize gradient histograms in TensorBoard.
        `histogram_freq` must be greater than 0.
      batch_size: size of batch of inputs to feed to the network for histograms
        computation.
      write_images: whether to write model weights to visualize as image in
        TensorBoard.
      embeddings_freq: frequency (in epochs) at which selected embedding layers
        will be saved. If set to 0, embeddings won't be computed. Data to be
        visualized in TensorBoard's Embedding tab must be passed as
        `embeddings_data`.
      embeddings_layer_names: a list of names of layers to keep eye on. If None
        or empty list all the embedding layer will be watched.
      embeddings_metadata: a dictionary which maps layer name to a file name in
        which metadata for this embedding layer is saved.
          [Here are details](
            https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
            about metadata files format. In case if the same metadata file is
            used for all embedding layers, string can be passed.
      embeddings_data: data to be embedded at layers specified in
        `embeddings_layer_names`. Numpy array (if the model has a single input)
        or list of Numpy arrays (if the model has multiple inputs). Learn more
        about embeddings [in this guide](
          https://www.tensorflow.org/programmers_guide/embedding).
      update_freq: `'batch'` or `'epoch'` or integer. When using `'batch'`,
        writes the losses and metrics to TensorBoard after each batch. The same
        applies for `'epoch'`. If using an integer, let's say `1000`, the
        callback will write the metrics and losses to TensorBoard every 1000
        samples. Note that writing too frequently to TensorBoard can slow down
        your training.
      profile_batch: Profile the batch to sample compute characteristics. By
        default, it will profile the second batch. Set profile_batch=0 to
        disable profiling.
  
  Raises:
      ValueError: If histogram_freq is set and no validation data is provided.
  
  @compatibility(eager)
  Using the `TensorBoard` callback will work when eager execution is enabled,
  with the restriction that outputting histogram summaries of weights and
  gradients is not supported. Consequently, `histogram_freq` will be ignored.
  @end_compatibility
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code: def on_test_begin(self, logs=None):
      pass
  
imports: import os
  import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import summary_ops_v2
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.profiler import profiler_v2
  from tensorflow.python.summary import summary
  from tensorflow.python.training import saver
label_desc: Use image node_id 9 for calling the TensorBoard obj's underlying member method code with example usage: obj.on_test_begin(logs) without return types