class_docstr: Adam with SGD updates every n steps with accumulated gradients.
class_docstr_tok: ["Adam","with","SGD","updates","every","n","steps","with","accumulated","gradients","."]
code: def __init__(
      self,
      learning_rate=0.001,
      beta1=0.9,
      beta2=0.999,
      epsilon=1e-8,
      use_locking=False,
      name="Adam",
      n=1,
  ):
      super(MultistepAdamOptimizer, self).__init__(
          learning_rate=learning_rate,
          beta1=beta1,
          beta2=beta2,
          epsilon=epsilon,
          use_locking=use_locking,
          name=name,
      )
      self._n = n  # Call Adam optimizer every n batches with accumulated grads
      self._n_t = None
  
file_path: tensor2tensor/tensor2tensor/utils/multistep_optimizer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new MultistepAdamOptimizer object from inherited base classes: tf.train with example: obj = MultistepAdamOptimizer(learning_rate, beta1, beta2, epsilon, use_locking, name, n)