code: def convert_to_apex_amp(
      model: nn.Module, optimizer: Optimizer, amp_config
  ):
      r"""A helper function to wrap training components with Apex AMP modules
  
      Args:
          model (:class:`torch.nn.Module`): your model object.
          optimizer (:class:`torch.optim.Optimizer`): your optimizer object.
          amp_config (Union[:class:`colossalai.context.Config`, dict]): configuration for initializing apex_amp.
  
      Returns:
          Tuple: A tuple (model, optimizer).
  
      The ``amp_config`` should include parameters below:
      ::
  
          enabled (bool, optional, default=True)
          opt_level (str, optional, default="O1")
          cast_model_type (``torch.dtype``, optional, default=None)
          patch_torch_functions (bool, optional, default=None)
          keep_batchnorm_fp32 (bool or str, optional, default=None
          master_weights (bool, optional, default=None)
          loss_scale (float or str, optional, default=None)
          cast_model_outputs (torch.dtype, optional, default=None)
          num_losses (int, optional, default=1)
          verbosity (int, default=1)
          min_loss_scale (float, default=None)
          max_loss_scale (float, default=2.**24)
  
      More details about ``amp_config`` refer to `amp_config <https://nvidia.github.io/apex/amp.html?highlight=apex%20amp>`_.
      """
      import apex.amp as apex_amp
  
      model, optimizer = apex_amp.initialize(
          model, optimizer, **amp_config
      )
      optimizer = ApexAMPOptimizer(optimizer)
      return model, optimizer
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_apex_amp(model, optimizer, amp_config) and returns: model,  optimizer