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 _create_slots(self, var_list):
      first_var = min(var_list, key=lambda x: x.name)
  
      create_new = self._beta1_power is None
      if not create_new and context.in_graph_mode():
          create_new = self._beta1_power.graph is not first_var.graph
  
      if create_new:
          with ops.colocate_with(first_var):
              self._beta1_power = variable_scope.variable(
                  self._beta1, name="beta1_power", trainable=False
              )
              self._beta2_power = variable_scope.variable(
                  self._beta2, name="beta2_power", trainable=False
              )
      # Create slots for the first and second moments.
      for v in var_list:
          self._zeros_slot(v, "m", self._name)
          self._zeros_slot(v, "v", self._name)
          self._zeros_slot(v, "vhat", self._name)
  
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 2 for calling the AMSGrad obj's underlying member method code with example usage: obj._create_slots(var_list) without return types