code: def forward(self, inputs):
      n_args = len(inputs)
      # TODO(kataoka): Do not retain inputs if n_args == 1
      self.retain_inputs(tuple(range(n_args)))
  
      xp = backend.get_array_module(inputs[0])
      dtype = xp.result_type(*[x.dtype for x in inputs])
  
      out_set = set(self.out_sub)
  
      # '@' is a single char, ',' is excluded.
      io_set = out_set.intersection(set(self.in_subs))
  
      if len(io_set) == len(self.out_sub):
          y = _einsum(xp, dtype, self.in_subs, self.out_sub, *inputs)
      else:
          direct_sub = []
          inverse_sub = []
          expander = []
          for c in sorted(out_set):
              if c in io_set:
                  direct_sub.append(c)
                  expander.append(slice(None))
              else:
                  expander.append(None)
              inverse_sub.append(c)
  
          y = xp.zeros(self.out_shape, dtype)
          diag_y = _einsum(
              xp, dtype, self.out_sub, "".join(inverse_sub), y
          )
          if diag_y.base is not y:
              raise ValueError("Update CuPy to close CuPy Issue #1199")
          # Make the view writeable as numpy PR #5410 for numpy<1.10.
          if xp is not cuda.cupy:  # no setflags in cupy
              diag_y.setflags(write=True)
          diag_y[...] = _einsum(
              xp, dtype, self.in_subs, "".join(direct_sub), *inputs
          )[tuple(expander)]
      return (y,)
  
imports: import warnings
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the DiagEinSum obj's underlying member method code with example usage: obj.forward(inputs) and returns: y