code: def load_weights(checkpoint, hf_model, config):
      hf_model.apply_weight_norm()
  
      hf_model.conv_pre.weight_g.data = checkpoint[
          "input_conv.weight_g"
      ]
      hf_model.conv_pre.weight_v.data = checkpoint[
          "input_conv.weight_v"
      ]
      hf_model.conv_pre.bias.data = checkpoint["input_conv.bias"]
  
      for i in range(len(config.upsample_rates)):
          hf_model.upsampler[i].weight_g.data = checkpoint[
              f"upsamples.{i}.1.weight_g"
          ]
          hf_model.upsampler[i].weight_v.data = checkpoint[
              f"upsamples.{i}.1.weight_v"
          ]
          hf_model.upsampler[i].bias.data = checkpoint[
              f"upsamples.{i}.1.bias"
          ]
  
      for i in range(
          len(config.upsample_rates) * len(config.resblock_kernel_sizes)
      ):
          for j in range(len(config.resblock_dilation_sizes)):
              hf_model.resblocks[i].convs1[
                  j
              ].weight_g.data = checkpoint[
                  f"blocks.{i}.convs1.{j}.1.weight_g"
              ]
              hf_model.resblocks[i].convs1[
                  j
              ].weight_v.data = checkpoint[
                  f"blocks.{i}.convs1.{j}.1.weight_v"
              ]
              hf_model.resblocks[i].convs1[j].bias.data = checkpoint[
                  f"blocks.{i}.convs1.{j}.1.bias"
              ]
  
              hf_model.resblocks[i].convs2[
                  j
              ].weight_g.data = checkpoint[
                  f"blocks.{i}.convs2.{j}.1.weight_g"
              ]
              hf_model.resblocks[i].convs2[
                  j
              ].weight_v.data = checkpoint[
                  f"blocks.{i}.convs2.{j}.1.weight_v"
              ]
              hf_model.resblocks[i].convs2[j].bias.data = checkpoint[
                  f"blocks.{i}.convs2.{j}.1.bias"
              ]
  
      hf_model.conv_post.weight_g.data = checkpoint[
          "output_conv.1.weight_g"
      ]
      hf_model.conv_post.weight_v.data = checkpoint[
          "output_conv.1.weight_v"
      ]
      hf_model.conv_post.bias.data = checkpoint["output_conv.1.bias"]
  
      hf_model.remove_weight_norm()
  
imports: import argparse
  import numpy
  import torch
  from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig, logging
label_desc: Use image node_id 1 for calling a global function with example usage: load_weights(checkpoint, hf_model, config) without return types