code: def auto_fp16(apply_to=None, out_fp32=False):
      """Decorator to enable fp16 training automatically.
  
      This decorator is useful when you write custom modules and want to support
      mixed precision training. If inputs arguments are fp32 tensors, they will
      be converted to fp16 automatically. Arguments other than fp32 tensors are
      ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
      backend, otherwise, original mmcv implementation will be adopted.
  
      Args:
          apply_to (Iterable, optional): The argument names to be converted.
              `None` indicates all arguments.
          out_fp32 (bool): Whether to convert the output back to fp32.
  
      Example:
  
          >>> import torch.nn as nn
          >>> class MyModule1(nn.Module):
          >>>
          >>>     # Convert x and y to fp16
          >>>     @auto_fp16()
          >>>     def forward(self, x, y):
          >>>         pass
  
          >>> import torch.nn as nn
          >>> class MyModule2(nn.Module):
          >>>
          >>>     # convert pred to fp16
          >>>     @auto_fp16(apply_to=('pred', ))
          >>>     def do_something(self, pred, others):
          >>>         pass
      """
  
      def auto_fp16_wrapper(old_func):
          @functools.wraps(old_func)
          def new_func(*args, **kwargs):
              # check if the module has set the attribute `fp16_enabled`, if not,
              # just fallback to the original method.
              if not isinstance(args[0], torch.nn.Module):
                  raise TypeError(
                      "@auto_fp16 can only be used to decorate the "
                      "method of nn.Module"
                  )
              if not (
                  hasattr(args[0], "fp16_enabled")
                  and args[0].fp16_enabled
              ):
                  return old_func(*args, **kwargs)
  
              # get the arg spec of the decorated method
              args_info = getfullargspec(old_func)
              # get the argument names to be casted
              args_to_cast = (
                  args_info.args if apply_to is None else apply_to
              )
              # convert the args that need to be processed
              new_args = []
              # NOTE: default args are not taken into consideration
              if args:
                  arg_names = args_info.args[: len(args)]
                  for i, arg_name in enumerate(arg_names):
                      if arg_name in args_to_cast:
                          new_args.append(
                              cast_tensor_type(
                                  args[i], torch.float, torch.half
                              )
                          )
                      else:
                          new_args.append(args[i])
              # convert the kwargs that need to be processed
              new_kwargs = {}
              if kwargs:
                  for arg_name, arg_value in kwargs.items():
                      if arg_name in args_to_cast:
                          new_kwargs[arg_name] = cast_tensor_type(
                              arg_value, torch.float, torch.half
                          )
                      else:
                          new_kwargs[arg_name] = arg_value
              # apply converted arguments to the decorated method
              if TORCH_VERSION != "parrots" and digit_version(
                  TORCH_VERSION
              ) >= digit_version("1.6.0"):
                  with autocast(enabled=True):
                      output = old_func(*new_args, **new_kwargs)
              else:
                  output = old_func(*new_args, **new_kwargs)
              # cast the results back to fp32 if necessary
              if out_fp32:
                  output = cast_tensor_type(
                      output, torch.half, torch.float
                  )
              return output
  
          return new_func
  
      return auto_fp16_wrapper
  
imports: import functools
  import warnings
  from collections import abc
  from inspect import getfullargspec
  import numpy
  import torch
  import torch.nn
  from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
  from .dist_utils import allreduce_grads
label_desc: Use image node_id 2 for calling a global function with example usage: auto_fp16(apply_to, out_fp32) and returns: auto_fp16_wrapper, new_func, output, old_func