class_docstr: Long short-term memory unit with forget gate.
  
  It has two inputs (c, x) and two outputs (c, h), where c indicates the cell
  state. x must have four times channels compared to the number of units.
class_docstr_tok: ["Long","short-term","memory","unit","with","forget","gate",".","It","has","two","inputs","(","c",",","x",")","and","two","outputs","(","c",",","h",")",",","where","c","indicates","the","cell","state",".","x","must","have","four","times","channels","compared","to","the","number","of","units","."]
code: def forward_chainerx(self, inputs):
      c, x = inputs
      c_next, h = chainerx.lstm(c, x)
      return c_next, h
  
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import function
  from chainer import function_node
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 2 for calling the LSTM obj's underlying member method code with example usage: obj.forward_chainerx(inputs) and returns: c_next,  h