code: def mLSTMCell(
      input, hidden, w_ih, w_hh, w_mih, w_mhh, b_ih=None, b_hh=None
  ):
      """
      mLSTMCell
      """
  
      if input.is_cuda:
          igates = F.linear(input, w_ih)
          m = F.linear(input, w_mih) * F.linear(hidden[0], w_mhh)
          hgates = F.linear(m, w_hh)
  
          state = fusedBackend.LSTMFused.apply
          return state(igates, hgates, hidden[1], b_ih, b_hh)
  
      hx, cx = hidden
  
      m = F.linear(input, w_mih) * F.linear(hidden[0], w_mhh)
      gates = F.linear(input, w_ih, b_ih) + F.linear(m, w_hh, b_hh)
  
      ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
  
      ingate = F.sigmoid(ingate)
      forgetgate = F.sigmoid(forgetgate)
      cellgate = F.tanh(cellgate)
      outgate = F.sigmoid(outgate)
  
      cy = (forgetgate * cx) + (ingate * cellgate)
      hy = outgate * F.tanh(cy)
  
      return hy, cy
  
file_path: apex/apex/RNN/cells.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .RNNBackend import RNNCell
  from torch.nn._functions.thnn import rnnFusedPointwise
  import math
label_desc: Use image node_id 1 for calling a global function with example usage: mLSTMCell(input, hidden, w_ih, w_hh, w_mih, w_mhh, b_ih, b_hh) and returns: hy,  cy, state