code: def deallocate_output_tensor(out, deallocate_pipeline_outputs=False):
      """Pseudo-deallocate (i.e., set to scalar) the output tensor's '.data' field.
  
      This method should be called right after the output tensor has been
      sent to the next pipeline stage. At this point, the output tensor is
      only useful for its '.grad_fn' field, and not its '.data'.
      """
      if (out is None) or (not deallocate_pipeline_outputs):
          return
      assert isinstance(out, torch.Tensor), (
          "expected Tensor, found %s." % type(out).__name__
      )
      assert (
          out._base is None
      ), "counter-productive to free a view of another tensor."
      out.data = torch.empty(
          (1,),
          device=out.device,
          dtype=out.dtype,
      )
  
imports: import contextlib
  from typing import Callable, Iterator, List, Optional, Union
  import torch
  from torch.autograd.variable import Variable
  from megatron.core import parallel_state
  from megatron.core.enums import ModelType
  from megatron.core.pipeline_parallel import p2p_communication
  from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type
label_desc: Use image node_id 2 for calling a global function with example usage: deallocate_output_tensor(out, deallocate_pipeline_outputs) without return types