code: def hifigan_vocoder(
      in_channels: int,
      upsample_rates: Tuple[int, ...],
      upsample_initial_channel: int,
      upsample_kernel_sizes: Tuple[int, ...],
      resblock_kernel_sizes: Tuple[int, ...],
      resblock_dilation_sizes: Tuple[Tuple[int, ...], ...],
      resblock_type: int,
      lrelu_slope: float,
  ) -> HiFiGANVocoder:
      r"""Builds HiFi GAN Vocoder :cite:`NEURIPS2020_c5d73680`.
  
      Args:
          in_channels (int): See :py:class:`HiFiGANVocoder`.
          upsample_rates (tuple of ``int``): See :py:class:`HiFiGANVocoder`.
          upsample_initial_channel (int): See :py:class:`HiFiGANVocoder`.
          upsample_kernel_sizes (tuple of ``int``): See :py:class:`HiFiGANVocoder`.
          resblock_kernel_sizes (tuple of ``int``): See :py:class:`HiFiGANVocoder`.
          resblock_dilation_sizes (tuple of tuples of ``int``): See :py:class:`HiFiGANVocoder`.
          resblock_type (int, 1 or 2): See :py:class:`HiFiGANVocoder`.
      Returns:
          HiFiGANVocoder: generated model.
      """
  
      return HiFiGANVocoder(
          upsample_rates=upsample_rates,
          resblock_kernel_sizes=resblock_kernel_sizes,
          resblock_dilation_sizes=resblock_dilation_sizes,
          resblock_type=resblock_type,
          upsample_initial_channel=upsample_initial_channel,
          upsample_kernel_sizes=upsample_kernel_sizes,
          in_channels=in_channels,
          lrelu_slope=lrelu_slope,
      )
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d, ConvTranspose1d
label_desc: Use image node_id 2 for calling a global function with example usage: hifigan_vocoder(in_channels, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, resblock_kernel_sizes, resblock_dilation_sizes, resblock_type, lrelu_slope) and returns: HiFiGANVocoder