original_docstring: 
          Given the gradient, step with the outer optimizer using the gradient.
          Assumed that the gradient is a tuple/list of size compatible with model.parameters()
          If flat_grad, then the gradient is a flattened vector
          
docstring: Given the gradient, step with the outer optimizer using the gradient.
  Assumed that the gradient is a tuple/list of size compatible with model.parameters()
  If flat_grad, then the gradient is a flattened vector
docstring_tokens: Given the gradient step with the outer optimizer using the gradient . Assumed that the gradient is a tuple / list of size compatible with model . parameters () If flat_grad then the gradient is a flattened vector
code: def implicit_reward_update(self, grad, flat_grad=True):
          """
          Given the gradient, step with the outer optimizer using the gradient.
          Assumed that the gradient is a tuple/list of size compatible with model.parameters()
          If flat_grad, then the gradient is a flattened vector
          """
          if flat_grad:
              offset = 0
              grad = to_device(grad, self.config.use_gpu)
              for p in self.rew_pred.parameters():
                  this_grad = grad[offset:offset + p.nelement()].view(p.size())
                  p.grad = this_grad
                  offset += p.nelement()
              self.rew_opt.step()
code_tokens: def implicit_reward_update ( self , grad , flat_grad = True ) : if flat_grad : offset = 0 grad = to_device ( grad , self . config . use_gpu ) for p in self . rew_pred . parameters ( ) : this_grad = grad [ offset : offset + p . nelement ( ) ] . view ( p . size ( ) ) p . grad = this_grad offset += p . nelement ( ) self . rew_opt . step ( )
short_docstring: Given the gradient, step with the outer optimizer using the gradient.
short_docstring_tokens: Given the gradient step with the outer optimizer using the gradient .
comment: """
          Given the gradient, step with the outer optimizer using the gradient.
          Assumed that the gradient is a tuple/list of size compatible with model.parameters()
          If flat_grad, then the gradient is a flattened vector
          """