parameters: (self, loss)
return_statement: return [self._get_optimizer().apply_gradients(zip(grads, my_vars))]
docstring: Returns the ops to run to perform a training step on this estimator.
  
      Args:
        loss: The loss to use when calculating gradients.
  
      Returns:
        The ops to run to perform a training step.
docstring_summary: Returns the ops to run to perform a training step on this estimator.
docstring_tokens: Returns the ops to run to perform a training step on this estimator .
function: def get_train_step(self, loss):
      """Returns the ops to run to perform a training step on this estimator.
  
      Args:
        loss: The loss to use when calculating gradients.
  
      Returns:
        The ops to run to perform a training step.
      """
      my_vars = self._get_vars()
      if not (self._get_feature_columns() or my_vars):
        return []
  
      grads = gradients.gradients(loss, my_vars)
      if self._gradient_clip_norm:
        grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)
      return [self._get_optimizer().apply_gradients(zip(grads, my_vars))]
function_tokens: def get_train_step ( self , loss ) : my_vars = self . _get_vars ( ) if not ( self . _get_feature_columns ( ) or my_vars ) : return [ ] grads = gradients . gradients ( loss , my_vars ) if self . _gradient_clip_norm : grads , _ = clip_ops . clip_by_global_norm ( grads , self . _gradient_clip_norm ) return [ self . _get_optimizer ( ) . apply_gradients ( zip ( grads , my_vars ) ) ]