code: def backward_pass(
      self, y: torch.Tensor, dy: torch.Tensor, f_args={}, g_args={}
  ):  # pragma: no cover  # this is covered, but called directly from C++
      y1, y2 = torch.chunk(y, 2, dim=self.split_dim)
      del y
  
      dy1, dy2 = torch.chunk(dy, 2, dim=self.split_dim)
      del dy
  
      with torch.enable_grad():
          y1.requires_grad = True
          gy1 = self.g(y1, set_rng=True, **g_args)
          torch.autograd.backward(gy1, dy2)
  
      with torch.no_grad():
          x2 = y2 - gy1
          del y2, gy1
  
          dx1 = dy1 + y1.grad
          del dy1
          y1.grad = None
  
      with torch.enable_grad():
          x2.requires_grad = True
          fx2 = self.f(x2, set_rng=True, **f_args)
          torch.autograd.backward(fx2, dx1)
  
      with torch.no_grad():
          x1 = y1 - fx2
          del y1, fx2
  
          dx2 = dy2 + x2.grad
          del dy2
          x2.grad = None
  
          x = torch.cat([x1, x2.detach()], dim=self.split_dim)
          dx = torch.cat([dx1, dx2], dim=self.split_dim)
  
      return x, dx
  
imports: from typing import List
  import torch
  import torch.nn
  from torch.autograd.function import Function
  from torch.utils.checkpoint import get_device_states, set_device_states
  from xformers.components import RequiresWrappedInputs
label_desc: Use image node_id 3 for calling the ReversibleBlock obj's underlying member method code with example usage: obj.backward_pass(y, dy, f_args, g_args) and returns: x,  dx