code: def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden):
      
      param_cells = []
      last_states = []
      for i in range(num_lstm_layer):
          param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i),
                                       i2h_bias=mx.sym.Variable("l%d_i2h_bias" % i),
                                       h2h_weight=mx.sym.Variable("l%d_h2h_weight" % i),
                                       h2h_bias=mx.sym.Variable("l%d_h2h_bias" % i)))
          state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i),
                            h=mx.sym.Variable("l%d_init_h" % i))
          last_states.append(state)
      assert len(last_states) == num_lstm_layer
  
      # embedding layer
      data = mx.sym.Variable('data')
      wordvec = mx.sym.SliceChannel(data=data, num_outputs=seq_len, squeeze_axis=1)
  
      hidden_all = []
      for seqidx in range(seq_len):
          hidden = wordvec[seqidx]
          for i in range(num_lstm_layer):
              next_state = _lstm(
                  num_hidden=num_hidden,
                  indata=hidden,
                  prev_state=last_states[i],
                  param=param_cells[i],
                  seqidx=seqidx,
                  layeridx=i)
              hidden = next_state.h
              last_states[i] = next_state
          hidden_all.append(hidden)
  
      hidden_concat = mx.sym.Concat(*hidden_all, dim=0)
      pred_fc = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11, name="pred_fc")
      return pred_fc
docs: Returns symbol for LSTM model up to loss/softmax