old_contents: import numpy
  
  from chainer import cuda
  from chainer import function
  from chainer.utils import type_check
  
  
  def _transpose(xs):
      xp = cuda.get_array_module(*xs)
      lengths = numpy.zeros(len(xs[0]), dtype='i')
      for i, x in enumerate(xs):
          lengths[0:len(x)] = i + 1
      dtype = xs[0].dtype
      unit = xs[0].shape[1:]
      outs = tuple(xp.empty((l,) + unit, dtype=dtype) for l in lengths)
  
      for i, x in enumerate(xs):
          for p, xi in enumerate(x):
              outs[p][i] = xi
  
      return outs
  
  
  class TransposeSequence(function.Function):
  
      def check_type_forward(self, xs_type):
          type_check.expect(xs_type.size() >= 1)
          for p, n in zip(xs_type, xs_type[1:]):
              type_check.expect(
                  p.shape[0] >= n.shape[0],
                  p.shape[1:] == n.shape[1:],
              )
  
      def forward(self, xs):
          return _transpose(xs)
  
      def backward(self, xs, gs):
          return _transpose(gs)
  
  
  def transpose_sequence(xs, force_tuple=True):
      ys = TransposeSequence()(*xs)
      if force_tuple and not isinstance(ys, tuple):
          ys = (ys,)
      return ys
  
new_contents: import numpy
  
  from chainer import cuda
  from chainer import function
  from chainer.utils import type_check
  
  
  def _transpose(xs):
      xp = cuda.get_array_module(*xs)
      lengths = numpy.zeros(len(xs[0]), dtype='i')
      for i, x in enumerate(xs):
          lengths[0:len(x)] = i + 1
      dtype = xs[0].dtype
      unit = xs[0].shape[1:]
      outs = tuple(xp.empty((l,) + unit, dtype=dtype) for l in lengths)
  
      for i, x in enumerate(xs):
          for p, xi in enumerate(x):
              outs[p][i] = xi
  
      return outs
  
  
  class TransposeSequence(function.Function):
  
      def check_type_forward(self, xs_type):
          type_check.expect(xs_type.size() >= 1)
          for p, n in zip(xs_type, xs_type[1:]):
              type_check.expect(
                  p.shape[0] >= n.shape[0],
                  p.shape[1:] == n.shape[1:],
              )
  
      def forward(self, xs):
          return _transpose(xs)
  
      def backward(self, xs, gs):
          return _transpose(gs)
  
  
  def transpose_sequence(xs):
      ys = TransposeSequence()(*xs)
      if not isinstance(ys, tuple):
          ys = (ys,)
      return ys
  
subject: Remove force_tuple arugment