class_docstr: Callback for Keras models.
docstr: Callback for logging performance.
  
  Args:
    batch_size: Total batch size.
    log_steps: Interval of steps between logging of batch level stats.
docstr_tok: ["Callback","for","logging","performance",".","Args",":","batch_size",":","Total","batch","size",".","log_steps",":","Interval","of","steps","between","logging","of","batch","level","stats","."]
code: def __init__(self, batch_size, log_steps):
      """Callback for logging performance.
  
      Args:
        batch_size: Total batch size.
        log_steps: Interval of steps between logging of batch level stats.
      """
      self.batch_size = batch_size
      super(TimeHistory, self).__init__()
      self.log_steps = log_steps
      self.global_steps = 0
  
      # Logs start of step 1 then end of each step based on log_steps
      # interval.
      self.timestamp_log = []
  
      # Records the time each epoch takes to run from start to finish of
      # epoch.
      self.epoch_runtime_log = []
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  import tensorflow
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python import tf2
  from tensorflow.python.eager import profiler
label_desc: Use image node_id 1 to create a new TimeHistory object from inherited base classes: tf.keras.callbacks with example: obj = TimeHistory(batch_size, log_steps)