class_docstr: Implementation of the AMSGrad optimization algorithm.
  
  See: `On the Convergence of Adam and Beyond - [Reddi et al., 2018] <https://openreview.net/pdf?id=ryQu7f-RZ>`__.
  
  Parameters
  ----------
  learning_rate: float
      A Tensor or a floating point value.  The learning rate.
  beta1: float
      A float value or a constant float tensor.
      The exponential decay rate for the 1st moment estimates.
  beta2: float
      A float value or a constant float tensor.
      The exponential decay rate for the 2nd moment estimates.
  epsilon: float
      A small constant for numerical stability.
      This epsilon is "epsilon hat" in the Kingma and Ba paper
      (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
  use_locking: bool
      If True use locks for update operations.
  name: str
      Optional name for the operations created when applying gradients.
      Defaults to "AMSGrad".
code: def _finish(self, update_ops, name_scope):
      # Update the power accumulators.
      with ops.control_dependencies(update_ops):
          with ops.colocate_with(self._beta1_power):
              update_beta1 = self._beta1_power.assign(
                  self._beta1_power * self._beta1_t,
                  use_locking=self._use_locking,
              )
              update_beta2 = self._beta2_power.assign(
                  self._beta2_power * self._beta2_t,
                  use_locking=self._use_locking,
              )
      return control_flow_ops.group(
          *update_ops + [update_beta1, update_beta2], name=name_scope
      )
  
file_path: TensorLayer/tensorlayer/optimizers/amsgrad.py
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import control_flow_ops, math_ops, resource_variable_ops, state_ops, variable_scope
  from tensorflow.python.training import optimizer
label_desc: Use image node_id 10 for calling the AMSGrad obj's underlying member method code with example usage: obj._finish(update_ops, name_scope) and returns: control_flow_ops