class_docstr: Base Optimizer class.
  
  A basic class to create optimizers to be used with TFLearn estimators.
  First, The Optimizer class is initialized with given parameters,
  but no Tensor is created. In a second step, invoking `get_tensor` method
  will actually build the Tensorflow `Optimizer` Tensor, and return it.
  
  This way, a user can easily specifies an optimizer with non default
  parameters and learning rate decay, while TFLearn estimators will
  build the optimizer and a step tensor by itself.
  
  Arguments:
      learning_rate: `float`. Learning rate.
      use_locking: `bool`. If True use locks for update operation.
      name: `str`. The optimizer name.
  
  Attributes:
      tensor: `Optimizer`. The optimizer tensor.
      has_decay: `bool`. True if optimizer has a learning rate decay.
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docstr: get_tensor.
  
  A method to retrieve the optimizer tensor.
  
  Returns:
      The `Optimizer`.
docstr_tok: ["get_tensor",".","A","method","to","retrieve","the","optimizer","tensor",".","Returns",":","The","`","Optimizer","`","."]
code: def get_tensor(self):
      """get_tensor.
  
      A method to retrieve the optimizer tensor.
  
      Returns:
          The `Optimizer`.
  
      """
      if not self.built:
          self.build()
      return self.tensor
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 3 for calling the Optimizer obj's underlying member method code with example usage: obj.get_tensor() and returns: self