code: def forward(
      ctx: Context,
      phony: Tensor,
      recomputed: Deque[Recomputed],
      rng_states: Deque[RNGStates],
      function: Function,
      input_atomic: bool,
      *input: Tensor,
  ) -> TensorOrTensors:
      ctx.recomputed = recomputed
      ctx.rng_states = rng_states
  
      save_rng_states(input[0].device, ctx.rng_states)
  
      ctx.function = function
      ctx.input_atomic = input_atomic
      ctx.save_for_backward(*input)
  
      with torch.no_grad(), enable_checkpointing():
          output = function(input[0] if input_atomic else input)
  
      return output
  
imports: from collections import deque
  from contextlib import contextmanager
  import threading
  from typing import TYPE_CHECKING, Deque, Generator, List, Optional, Tuple, Union
  import torch
  from torch import ByteTensor, Tensor
  import torch.autograd
  from .dependency import fork, join
  from .microbatch import Batch
  from .phony import get_phony
label_desc: Use image node_id 1 for calling the Checkpoint obj's underlying member method code with example usage: obj.forward(ctx, phony, recomputed, rng_states, function, input_atomic) and returns: output