parameters: (self, grad, local_step=0, name=None)
return_statement: return self.apply_grad(
          grad_indices=grad.indices,
          grad_values=grad.values,
          grad_shape=grad.dense_shape,
          local_step=local_step,
          name=name)
docstring: Attempts to apply a gradient to the accumulator.
  
      The attempt is silently dropped if the gradient is stale, i.e., local_step
      is less than the accumulator's global time step.
  
      Args:
        grad: The gradient IndexedSlices to be applied.
        local_step: Time step at which the gradient was computed.
        name: Optional name for the operation.
  
      Returns:
        The operation that (conditionally) applies a gradient to the accumulator.
  
      Raises:
        InvalidArgumentError: If grad is of the wrong shape
docstring_summary: Attempts to apply a gradient to the accumulator.
docstring_tokens: Attempts to apply a gradient to the accumulator .
function: def apply_indexed_slices_grad(self, grad, local_step=0, name=None):
      """Attempts to apply a gradient to the accumulator.
  
      The attempt is silently dropped if the gradient is stale, i.e., local_step
      is less than the accumulator's global time step.
  
      Args:
        grad: The gradient IndexedSlices to be applied.
        local_step: Time step at which the gradient was computed.
        name: Optional name for the operation.
  
      Returns:
        The operation that (conditionally) applies a gradient to the accumulator.
  
      Raises:
        InvalidArgumentError: If grad is of the wrong shape
      """
      return self.apply_grad(
          grad_indices=grad.indices,
          grad_values=grad.values,
          grad_shape=grad.dense_shape,
          local_step=local_step,
          name=name)
function_tokens: def apply_indexed_slices_grad ( self , grad , local_step = 0 , name = None ) : return self . apply_grad ( grad_indices = grad . indices , grad_values = grad . values , grad_shape = grad . dense_shape , local_step = local_step , name = name )