class_docstr: TreeLSTM unit with N forget gates.
  
  This have variable inputs ``(c1, c2, ..., cN, x)``
  where ``x`` is (3 + N) times larger than each cell.
  Forget gates ``(f1, f2, ..., fN)`` can depend in
  different partitions of ``x[:, 3 * cell_units:]``.
  There are two outputs ``(c, h)``.
class_docstr_tok: ["TreeLSTM","unit","with","N","forget","gates",".","This","have","variable","inputs","``","(","c1",",","c2",",","...",",","cN",",","x",")","``","where","``","x","``","is","(","3","+","N",")","times","larger","than","each","cell",".","Forget","gates","``","(","f1",",","f2",",","...",",","fN",")","``","can","depend","in","different","partitions","of","``","x","[",":",",","3","*","cell_units",":","]","``",".","There","are","two","outputs","``","(","c",",","h",")","``","."]
code: def forward_chainerx(self, inputs):
      return chainerx.tree_lstm(*inputs)
  
file_path: chainer/chainer/functions/rnn/tree_lstm.py
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 2 for calling the TreeLSTM obj's underlying member method code with example usage: obj.forward_chainerx(inputs) and returns: chainerx