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 _apply_dense_in_action(self, grad, var):
      m = self.get_slot(var, "m")
      v = self.get_slot(var, "v")
      beta1_power, beta2_power = self._get_beta_accumulators()
      return training_ops.apply_adam(
          var,
          m,
          v,
          tf.cast(beta1_power, var.dtype.base_dtype),
          tf.cast(beta2_power, var.dtype.base_dtype),
          tf.cast(self._lr_t, var.dtype.base_dtype),
          tf.cast(self._beta1_t, var.dtype.base_dtype),
          tf.cast(self._beta2_t, var.dtype.base_dtype),
          tf.cast(self._epsilon_t, var.dtype.base_dtype),
          grad,
          use_locking=self._use_locking,
      ).op
  
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 8 for calling the MultistepAdamOptimizer obj's underlying member method code with example usage: obj._apply_dense_in_action(grad, var) and returns: training_ops