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 _prepare(self):
      self._lr_t = ops.convert_to_tensor(self._lr)
      self._beta1_t = ops.convert_to_tensor(self._beta1)
      self._beta2_t = ops.convert_to_tensor(self._beta2)
      self._epsilon_t = ops.convert_to_tensor(self._epsilon)
  
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 3 for calling the AMSGrad obj's underlying member method code with example usage: obj._prepare() without return types