class_docstr: Logs training speed and evaluation metrics periodically.
  
  Parameters
  ----------
  batch_size: int
      Batch size of data.
  frequent: int
      Specifies how frequently training speed and evaluation metrics
      must be logged. Default behavior is to log once every 50 batches.
  auto_reset : bool
      Reset the evaluation metrics after each log.
  
  Example
  -------
  >>> # Print training speed and evaluation metrics every ten batches. Batch size is one.
  >>> module.fit(iterator, num_epoch=n_epoch,
  ... batch_end_callback=mx.callback.Speedometer(1, 10))
  Epoch[0] Batch [10] Speed: 1910.41 samples/sec  Train-accuracy=0.200000
  Epoch[0] Batch [20] Speed: 1764.83 samples/sec  Train-accuracy=0.400000
  Epoch[0] Batch [30] Speed: 1740.59 samples/sec  Train-accuracy=0.500000
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code: def __init__(self, batch_size, frequent=50, auto_reset=True):
      self.batch_size = batch_size
      self.frequent = frequent
      self.init = False
      self.tic = 0
      self.last_count = 0
      self.auto_reset = auto_reset
  
file_path: mxnet/python/mxnet/callback.py
imports: import logging
  import math
  import time
  from .model import save_checkpoint
label_desc: Use image node_id 1 to create a new Speedometer object from inherited base classes: object with example: obj = Speedometer(batch_size, frequent, auto_reset)