code: def __init__(
      self,
      input_sample_rate=22050,
      output_sample_rate=24000,
      output_hop_length=256,
      ar_mel_length_compression=1024,
      decoder_input_dim=1024,
      resblock_type_decoder="1",
      resblock_dilation_sizes_decoder=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
      resblock_kernel_sizes_decoder=[3, 7, 11],
      upsample_rates_decoder=[8, 8, 2, 2],
      upsample_initial_channel_decoder=512,
      upsample_kernel_sizes_decoder=[16, 16, 4, 4],
      d_vector_dim=512,
      cond_d_vector_in_each_upsampling_layer=True,
      speaker_encoder_audio_config={
          "fft_size": 512,
          "win_length": 400,
          "hop_length": 160,
          "sample_rate": 16000,
          "preemphasis": 0.97,
          "num_mels": 64,
      },
  ):
      super().__init__()
      self.input_sample_rate = input_sample_rate
      self.output_sample_rate = output_sample_rate
      self.output_hop_length = output_hop_length
      self.ar_mel_length_compression = ar_mel_length_compression
      self.speaker_encoder_audio_config = speaker_encoder_audio_config
      self.waveform_decoder = HifiganGenerator(
          decoder_input_dim,
          1,
          resblock_type_decoder,
          resblock_dilation_sizes_decoder,
          resblock_kernel_sizes_decoder,
          upsample_kernel_sizes_decoder,
          upsample_initial_channel_decoder,
          upsample_rates_decoder,
          inference_padding=0,
          cond_channels=d_vector_dim,
          conv_pre_weight_norm=False,
          conv_post_weight_norm=False,
          conv_post_bias=False,
          cond_in_each_up_layer=cond_d_vector_in_each_upsampling_layer,
      )
      self.speaker_encoder = ResNetSpeakerEncoder(
          input_dim=64,
          proj_dim=512,
          log_input=True,
          use_torch_spec=True,
          audio_config=speaker_encoder_audio_config,
      )
  
imports: import torch
  import torchaudio
  from torch import nn
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 1 to create a new HifiDecoder object from inherited base classes: torch.nn with example: obj = HifiDecoder(input_sample_rate, output_sample_rate, output_hop_length, ar_mel_length_compression, decoder_input_dim, resblock_type_decoder, resblock_dilation_sizes_decoder, resblock_kernel_sizes_decoder, upsample_rates_decoder, upsample_initial_channel_decoder, upsample_kernel_sizes_decoder, d_vector_dim, cond_d_vector_in_each_upsampling_layer, speaker_encoder_audio_config)