code: def build_fp16_loss_scaler(loss_scale: mmcv.Config) -> Any:
      """
      This function is derived from mmcv, which is coverd by the Apache 2.0 License.
      Copyright (c) OpenMMLab. All rights reserved.
  
      Arguments:
          loss_scale (float | str | dict): Scale factor configuration.
                      If loss_scale is a float, static loss scaling will be used with
                      the specified scale. If loss_scale is a string, it must be
                      'dynamic', then dynamic loss scaling will be used.
                      It can also be a dict containing arguments of GradScalar.
                      Defaults to 512. For PyTorch >= 1.6, mmcv uses official
                      implementation of GradScaler. If you use a dict version of
                      loss_scale to create GradScaler, please refer to:
                      https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler
                      for the parameters.
      Examples:
          >>> loss_scale = dict(
          ...     init_scale=65536.0,
          ...     growth_factor=2.0,
          ...     backoff_factor=0.5,
          ...     growth_interval=2000
          ... )
      """
      if loss_scale == "dynamic":
          loss_scaler = torch.cuda.amp.GradScaler()  # type: ignore
      elif isinstance(loss_scale, float):
          loss_scaler = torch.cuda.amp.GradScaler(init_scale=loss_scale)  # type: ignore
      elif isinstance(loss_scale, dict):
          loss_scaler = torch.cuda.amp.GradScaler(**loss_scale)  # type: ignore
      else:
          raise Exception(
              "Cannot parse fp16 configuration.  Expected cfg to be str(dynamic), float or dict."
          )
      return loss_scaler
  
file_path: determined/model_hub/model_hub/mmdetection/utils.py
imports: import os
  from typing import Any, Dict, Tuple
  import mmcv
  import torch
  import model_hub.utils
label_desc: Use image node_id 3 for calling a global function with example usage: build_fp16_loss_scaler(loss_scale) and returns: loss_scaler