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__(
          use_locking=use_locking, name=name
      )
      self._lr = learning_rate
      self._beta1 = beta1
      self._beta2 = beta2
      self._epsilon = epsilon
      # Tensor versions of the constructor arguments, created in _prepare().
      self._lr_t = None
      self._beta1_t = None
      self._beta2_t = None
      self._epsilon_t = None
      self._n = n  # Call Adam optimizer every n batches with accumulated grads
      self._n_t = None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.training import training_ops
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)