code: def __init__(
      self,
      in_dim,
      out_dim,
      n_layers=2,
      n_channels=256,
      kernel_size=3,
      p_dropout=0.1,
      lstm_type="bilstm",
      use_linear=True,
  ):
      super(
          ConvLSTMLinear, self
      ).__init__()  # pylint: disable=super-with-arguments
      self.out_dim = out_dim
      self.lstm_type = lstm_type
      self.use_linear = use_linear
      self.dropout = nn.Dropout(p=p_dropout)
  
      convolutions = []
      for i in range(n_layers):
          conv_layer = ConvNorm(
              in_dim if i == 0 else n_channels,
              n_channels,
              kernel_size=kernel_size,
              stride=1,
              padding=int((kernel_size - 1) / 2),
              dilation=1,
              w_init_gain="relu",
          )
          conv_layer = nn.utils.parametrizations.weight_norm(
              conv_layer.conv, name="weight"
          )
          convolutions.append(conv_layer)
  
      self.convolutions = nn.ModuleList(convolutions)
  
      if not self.use_linear:
          n_channels = out_dim
  
      if self.lstm_type != "":
          use_bilstm = False
          lstm_channels = n_channels
          if self.lstm_type == "bilstm":
              use_bilstm = True
              lstm_channels = int(n_channels // 2)
  
          self.bilstm = nn.LSTM(
              n_channels,
              lstm_channels,
              1,
              batch_first=True,
              bidirectional=use_bilstm,
          )
          lstm_norm_fn_pntr = nn.utils.spectral_norm
          self.bilstm = lstm_norm_fn_pntr(self.bilstm, "weight_hh_l0")
          if self.lstm_type == "bilstm":
              self.bilstm = lstm_norm_fn_pntr(
                  self.bilstm, "weight_hh_l0_reverse"
              )
  
      if self.use_linear:
          self.dense = nn.Linear(n_channels, out_dim)
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 1 to create a new ConvLSTMLinear object from inherited base classes: nn with example: obj = ConvLSTMLinear(in_dim, out_dim, n_layers, n_channels, kernel_size, p_dropout, lstm_type, use_linear)