docstr: Multiplies ('scales') a tensor or list of tensors by the scale factor.
  
  Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
  unmodified.
  
  Args:
      outputs (Tensor or iterable of Tensors):  Outputs to scale.
code: def scale(self, outputs):
      """
      Multiplies ('scales') a tensor or list of tensors by the scale factor.
  
      Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
      unmodified.
  
      Args:
          outputs (Tensor or iterable of Tensors):  Outputs to scale.
      """
      if not self._enabled:
          return outputs
  
      # Short-circuit for the common case.
      if isinstance(outputs, torch.Tensor):
          assert outputs.is_cuda or outputs.device.type == "xla"
          if self._scale is None:
              self._lazy_init_scale_growth_tracker(outputs.device)
          assert self._scale is not None
          return outputs * self._scale.to(
              device=outputs.device, non_blocking=True
          )
  
      # Invoke the more complex machinery only if we're treating multiple outputs.
      # holds a reference that can be overwritten by apply_scale
      stash: List[_MultiDeviceReplicator] = []
  
      def apply_scale(val):
          if isinstance(val, torch.Tensor):
              assert val.is_cuda or val.device.type == "xla"
              if len(stash) == 0:
                  if self._scale is None:
                      self._lazy_init_scale_growth_tracker(val.device)
                  assert self._scale is not None
                  stash.append(_MultiDeviceReplicator(self._scale))
              return val * stash[0].get(val.device)
          elif isinstance(val, abc.Iterable):
              iterable = map(apply_scale, val)
              if isinstance(val, list) or isinstance(val, tuple):
                  return type(val)(iterable)
              else:
                  return iterable
          else:
              raise ValueError(
                  "outputs must be a Tensor or an iterable of Tensors"
              )
  
      return apply_scale(outputs)
  
imports: import warnings
  from collections import abc, defaultdict
  from enum import Enum
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.distributed
  from packaging import version
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
label_desc: Use image node_id 4 for calling the GradScaler obj's underlying member method code with example usage: obj.scale(outputs) and returns: apply_scale, outputs, unknown, unknown, type, iterable