docstr: Initialize harmonic generator module.
  
  Args:
      hidden_channels (int): Number of channels in the input and hidden layers.
      n_harmonic (int): Number of harmonic channels.
      kernel_size (int): Size of the convolutional kernel.
      padding (int): Amount of padding added to the input.
      dropout_rate (float): Dropout rate.
      sample_rate (int): Sampling rate of the input audio.
      hop_size (int): Hop size used in the analysis of the input audio.
code: def __init__(
      self,
      hidden_channels: int = 192,
      n_harmonic: int = 64,
      kernel_size: int = 3,
      padding: int = 1,
      dropout_rate: float = 0.1,
      sample_rate: int = 22050,
      hop_size: int = 256,
  ):
      """Initialize harmonic generator module.
  
      Args:
          hidden_channels (int): Number of channels in the input and hidden layers.
          n_harmonic (int): Number of harmonic channels.
          kernel_size (int): Size of the convolutional kernel.
          padding (int): Amount of padding added to the input.
          dropout_rate (float): Dropout rate.
          sample_rate (int): Sampling rate of the input audio.
          hop_size (int): Hop size used in the analysis of the input audio.
  
      """
      super().__init__()
  
      self.prenet = torch.nn.Conv1d(
          hidden_channels, hidden_channels, kernel_size, padding=padding
      )
  
      self.net = ConvReluNorm(
          hidden_channels,
          hidden_channels,
          hidden_channels,
          kernel_size,
          8,
          dropout_rate,
      )
  
      self.postnet = torch.nn.Conv1d(
          hidden_channels, n_harmonic + 1, kernel_size, padding=padding
      )
  
      self.sample_rate = sample_rate
      self.hop_size = hop_size
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 1 to create a new Generator_Harm object from inherited base classes: torch.nn with example: obj = Generator_Harm(hidden_channels, n_harmonic, kernel_size, padding, dropout_rate, sample_rate, hop_size)