code: def forward(
      ctx,
      run_function,
      length_tensors,
      length_non_tensors,
      tensor_keys,
      non_tensor_keys,
      *args,
  ):
      ctx.end_tensors = length_tensors
      ctx.end_non_tensors = length_tensors + length_non_tensors
      ctx.gpu_autocast_kwargs = {
          "enabled": torch.is_autocast_enabled(),
          "dtype": torch.get_autocast_gpu_dtype(),
          "cache_enabled": torch.is_autocast_cache_enabled(),
      }
      assert (
          len(tensor_keys) == length_tensors
          and len(non_tensor_keys) == length_non_tensors
      )
  
      ctx.input_tensors = {
          key: val
          for (key, val) in zip(
              tensor_keys, list(args[: ctx.end_tensors])
          )
      }
      ctx.input_non_tensors = {
          key: val
          for (key, val) in zip(
              non_tensor_keys,
              list(args[ctx.end_tensors : ctx.end_non_tensors]),
          )
      }
      ctx.run_function = run_function
      ctx.input_params = list(args[ctx.end_non_tensors :])
  
      with torch.no_grad():
          output_tensors = ctx.run_function(
              **ctx.input_tensors, **ctx.input_non_tensors
          )
      return output_tensors
  
imports: import math
  from typing import Optional
  import torch
  import torch.nn
  from einops import rearrange, repeat
label_desc: Use image node_id 1 for calling the MixedCheckpointFunction obj's underlying member method code with example usage: obj.forward(ctx, run_function, length_tensors, length_non_tensors, tensor_keys, non_tensor_keys) and returns: output_tensors