code: def __init__(
      self,
      encoder_embed_dim,
      var_pred_hidden_dim,
      var_pred_kernel_size,
      var_pred_dropout,
  ):
      super().__init__()
      self.conv1 = nn.Sequential(
          nn.Conv1d(
              encoder_embed_dim,
              var_pred_hidden_dim,
              kernel_size=var_pred_kernel_size,
              padding=(var_pred_kernel_size - 1) // 2,
          ),
          nn.ReLU(),
      )
      self.ln1 = nn.LayerNorm(var_pred_hidden_dim)
      self.dropout = var_pred_dropout
      self.conv2 = nn.Sequential(
          nn.Conv1d(
              var_pred_hidden_dim,
              var_pred_hidden_dim,
              kernel_size=var_pred_kernel_size,
              padding=1,
          ),
          nn.ReLU(),
      )
      self.ln2 = nn.LayerNorm(var_pred_hidden_dim)
      self.proj = nn.Linear(var_pred_hidden_dim, 1)
  
file_path: textlesslib/textless/vocoders/hifigan/generator.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, weight_norm
  from .resblock import ResBlock, init_weights, LRELU_SLOPE
label_desc: Use image node_id 1 to create a new VariancePredictor object from inherited base classes: nn with example: obj = VariancePredictor(encoder_embed_dim, var_pred_hidden_dim, var_pred_kernel_size, var_pred_dropout)