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 _resource_scatter_add(self, x, i, v):
      with ops.control_dependencies(
          [resource_variable_ops.resource_scatter_add(x.handle, i, v)]
      ):
          return x.value()
  
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 8 for calling the AMSGrad obj's underlying member method code with example usage: obj._resource_scatter_add(x, i, v) and returns: x