code: def __init__(self, enc_kernel_size: int, enc_feat_dim: int):
      super().__init__()
      # setting 50% overlap
      self.conv = nn.Conv1d(
          1,
          enc_feat_dim,
          kernel_size=enc_kernel_size,
          stride=enc_kernel_size // 2,
          bias=False,
      )
      self.nonlinear = nn.ReLU()
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.dpmulcat import DPMulCat
  from espnet2.enh.layers.dprnn import merge_feature, split_feature
  from espnet2.enh.separator.abs_separator import AbsSeparator
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: nn with example: obj = Encoder(enc_kernel_size, enc_feat_dim)