class_docstr: Implementation of an LSTM model for classification
  
  :param input_dim (int): Size of the vocabulary
  :param embed_dim (int): Embedding dimension (word vector size)
  :param hidden_dim (int): Hidden dimension for LSTM layers
  :param num_classes (int): Number of classes for classification
  :param dropout_prob (int): Dropout probability
  :param bidirectional (int): True if bidirectional LSTM needed
  :param batch_size (int): Batch size of input
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code: def forward(self, x, x_len):
      embed_out = self.dropout(self.embedding(x))
  
      packed_embed_out = nn.utils.rnn.pack_padded_sequence(
          embed_out, x_len, batch_first=True, enforce_sorted=False
      )
      _, (hidden, cell) = self.lstm(packed_embed_out)
  
      hidden = self.dropout(
          torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)
      )
      fc_out = self.fc(hidden)
  
      return fc_out
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the LSTMNet obj's underlying member method code with example usage: obj.forward(x, x_len) and returns: fc_out