class_docstr: Optimizer that implements the YellowFin algorithm.
  
  Implemented as a wrapper around tf.train.MomentumOptimizer
class_docstr_tok: ["Optimizer","that","implements","the","YellowFin","algorithm",".","Implemented","as","a","wrapper","around","tf.train.MomentumOptimizer"]
docstr: Add operations to minimize `loss` by updating `var_list`.
  
  This method simply combines calls `compute_gradients()` and
  `apply_gradients()`. If you want to process the gradient before
  applying them, call `tf.gradients()` and `self.apply_gradients()`
  explicitly instead of using this function.
  
  Adapted from Tensorflow Optimizer base class member function.
docstr_tok: ["Add","operations","to","minimize","`","loss","`","by","updating","`","var_list","`",".","This","method","simply","combines","calls","`","compute_gradients","(",")","`","and","`","apply_gradients","(",")","`",".","If","you","want","to","process","the","gradient","before","applying","them",",","call","`","tf.gradients","(",")","`","and","`","self.apply_gradients","(",")","`","explicitly","instead","of","using","this","function",".","Adapted","from","Tensorflow","Optimizer","base","class","member","function","."]
code: def minimize(
      self,
      loss,
      global_step=None,
      var_list=None,
      gate_gradients=GATE_OP,
      aggregation_method=None,
      colocate_gradients_with_ops=False,
      name=None,
      grad_loss=None,
  ):
      """Add operations to minimize `loss` by updating `var_list`.
  
      This method simply combines calls `compute_gradients()` and
      `apply_gradients()`. If you want to process the gradient before
      applying them, call `tf.gradients()` and `self.apply_gradients()`
      explicitly instead of using this function.
  
      Adapted from Tensorflow Optimizer base class member function.
      """
      grads_and_vars = self._optimizer.compute_gradients(
          loss,
          var_list=var_list,
          gate_gradients=gate_gradients,
          aggregation_method=aggregation_method,
          colocate_gradients_with_ops=colocate_gradients_with_ops,
          grad_loss=grad_loss,
      )
  
      vars_with_grad = [v for g, v in grads_and_vars if g is not None]
      if not vars_with_grad:
          raise ValueError(
              "No gradients provided for any variable, check your graph for "
              "ops that do not support gradients, between variables "
              "%s and loss %s."
              % ([str(v) for _, v in grads_and_vars], loss)
          )
  
      return self.apply_gradients(grads_and_vars, global_step, name)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import delta.compat
  from tensorflow.python.framework import ops
label_desc: Use image node_id 14 for calling the YFOptimizer obj's underlying member method code with example usage: obj.minimize(loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss) and returns: self