class_docstr: Optimizer that implements the YellowFin algorithm.
  
  See [Zhang et. al., 2017](https://arxiv.org/abs/1706.03471) for details.
docstr: Compute gradients through momentum optimizer.
  
  Args:
    loss: A Tensor containing the value to minimize.
    var_list: Optional list or tuple of tf.Variable to update
      to minimize loss. Defaults to the list of variables collected
      in the graph under the key GraphKey.TRAINABLE_VARIABLES.
    global_step: Optional Variable to increment by one after the
      variables have been updated.
    gate_gradients: How to gate the computation of gradients.
      Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
    aggregation_method: Specifies the method used to combine
      gradient terms. Valid values are defined in the class AggregationMethod.
    colocate_gradients_with_ops: If True, try collocating gradients with
      the corresponding op.
    name: Optional name for the returned operation. Default to the name
      passed to the Optimizer constructor.
    grad_loss: Optional. A Tensor holding the gradient computed for loss.
  
  Returns:
    A list of (gradient, variable) pairs. Variable is always present,
      but gradient can be None.
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code: def compute_gradients(
      self,
      loss,
      var_list,
      global_step=None,
      gate_gradients=GATE_OP,
      aggregation_method=None,
      colocate_gradients_with_ops=False,
      name=None,
      grad_loss=None,
  ):
      """Compute gradients through momentum optimizer.
  
      Args:
        loss: A Tensor containing the value to minimize.
        var_list: Optional list or tuple of tf.Variable to update
          to minimize loss. Defaults to the list of variables collected
          in the graph under the key GraphKey.TRAINABLE_VARIABLES.
        global_step: Optional Variable to increment by one after the
          variables have been updated.
        gate_gradients: How to gate the computation of gradients.
          Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
        aggregation_method: Specifies the method used to combine
          gradient terms. Valid values are defined in the class AggregationMethod.
        colocate_gradients_with_ops: If True, try collocating gradients with
          the corresponding op.
        name: Optional name for the returned operation. Default to the name
          passed to the Optimizer constructor.
        grad_loss: Optional. A Tensor holding the gradient computed for loss.
  
      Returns:
        A list of (gradient, variable) pairs. Variable is always present,
          but gradient can be None.
      """
      del global_step, name  # Unused for now.
      return self._momentum_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,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 13 for calling the YellowFinOptimizer obj's underlying member method code with example usage: obj.compute_gradients(loss, var_list, global_step, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss) and returns: self