code: def __init__(self, args):
      super().__init__()
      self.conv1 = nn.Sequential(
          nn.Conv1d(
              args.encoder_embed_dim,
              args.var_pred_hidden_dim,
              kernel_size=args.var_pred_kernel_size,
              padding=(args.var_pred_kernel_size - 1) // 2,
          ),
          nn.ReLU(),
      )
      self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim)
      self.dropout_module = FairseqDropout(
          p=args.var_pred_dropout, module_name=self.__class__.__name__
      )
      self.conv2 = nn.Sequential(
          nn.Conv1d(
              args.var_pred_hidden_dim,
              args.var_pred_hidden_dim,
              kernel_size=args.var_pred_kernel_size,
              padding=1,
          ),
          nn.ReLU(),
      )
      self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim)
      self.proj = nn.Linear(args.var_pred_hidden_dim, 1)
  
imports: import logging
  import torch
  from torch import nn
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet
  from fairseq.modules import FairseqDropout, LayerNorm, MultiheadAttention, PositionalEmbedding
label_desc: Use image node_id 1 to create a new VariancePredictor object from inherited base classes: nn with example: obj = VariancePredictor(args)