parameters: (self, *args, **kwargs)
return_statement: return self._opt.compute_gradients(*args, **kwargs)
docstring: Compute gradients of "loss" for the variables in "var_list".
  
      This simply wraps the compute_gradients() from the real optimizer. The
      gradients will be aggregated in the apply_gradients() so that user can
      modify the gradients like clipping with per replica global norm if needed.
      The global norm with aggregated gradients can be bad as one replica's huge
      gradients can hurt the gradients from other replicas.
  
      Args:
        *args: Arguments for compute_gradients().
        **kwargs: Keyword arguments for compute_gradients().
  
      Returns:
        A list of (gradient, variable) pairs.
docstring_summary: Compute gradients of "loss" for the variables in "var_list".
docstring_tokens: Compute gradients of loss for the variables in var_list .
function: def compute_gradients(self, *args, **kwargs):
      """Compute gradients of "loss" for the variables in "var_list".
  
      This simply wraps the compute_gradients() from the real optimizer. The
      gradients will be aggregated in the apply_gradients() so that user can
      modify the gradients like clipping with per replica global norm if needed.
      The global norm with aggregated gradients can be bad as one replica's huge
      gradients can hurt the gradients from other replicas.
  
      Args:
        *args: Arguments for compute_gradients().
        **kwargs: Keyword arguments for compute_gradients().
  
      Returns:
        A list of (gradient, variable) pairs.
      """
      return self._opt.compute_gradients(*args, **kwargs)
function_tokens: def compute_gradients ( self , * args , * * kwargs ) : return self . _opt . compute_gradients ( * args , * * kwargs )