code: def convert_weights(model: nn.Module):
      """Convert applicable model parameters to fp16"""
  
      def _convert_weights_to_fp16(l):
          if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
              l.weight.data = l.weight.data.half()
              if l.bias is not None:
                  l.bias.data = l.bias.data.half()
  
          if isinstance(l, nn.MultiheadAttention):
              for attr in [
                  *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
                  "in_proj_bias",
                  "bias_k",
                  "bias_v",
              ]:
                  tensor = getattr(l, attr)
                  if tensor is not None:
                      tensor.data = tensor.data.half()
  
          for name in ["text_projection", "proj"]:
              if hasattr(l, name):
                  attr = getattr(l, name)
                  if attr is not None:
                      attr.data = attr.data.half()
  
      model.apply(_convert_weights_to_fp16)
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 for calling a global function with example usage: convert_weights(model) without return types