code: def __init__(
      self,
      input_size,
      hidden_size,
      projection_dim=None,
      bidirectional=False,
      batch_first=False,
  ):
      super(NavieComplexLSTM, self).__init__()
      self.bidirectional = bidirectional
      self.input_dim = input_size // 2
      self.rnn_units = hidden_size // 2
      self.real_lstm = nn.LSTM(
          self.input_dim,
          self.rnn_units,
          num_layers=1,
          bidirectional=bidirectional,
          batch_first=False,
      )
      self.imag_lstm = nn.LSTM(
          self.input_dim,
          self.rnn_units,
          num_layers=1,
          bidirectional=bidirectional,
          batch_first=False,
      )
      if bidirectional:
          bidirectional = 2
      else:
          bidirectional = 1
      if projection_dim is not None:
          self.projection_dim = projection_dim // 2
          self.r_trans = nn.Linear(
              self.rnn_units * bidirectional, self.projection_dim
          )
          self.i_trans = nn.Linear(
              self.rnn_units * bidirectional, self.projection_dim
          )
      else:
          self.projection_dim = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new NavieComplexLSTM object from inherited base classes: nn with example: obj = NavieComplexLSTM(input_size, hidden_size, projection_dim, bidirectional, batch_first)