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 _prepare(self):
      lr = self._call_if_callable(self._lr)
      beta1 = self._call_if_callable(self._beta1)
      beta2 = self._call_if_callable(self._beta2)
      epsilon = self._call_if_callable(self._epsilon)
      self._beta1_t = tf.convert_to_tensor(beta1, name="beta1")
      self._beta2_t = tf.convert_to_tensor(beta2, name="beta2")
      self._lr_t = tf.convert_to_tensor(lr, name="learning_rate")
      self._epsilon_t = tf.convert_to_tensor(epsilon, name="epsilon")
      self._n_t = tf.convert_to_tensor(self._n, name="n")
  
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 5 for calling the MultistepAdamOptimizer obj's underlying member method code with example usage: obj._prepare() without return types