class_docstr: Softplus gradient function.
class_docstr_tok: ["Softplus","gradient","function","."]
code: def check_type_forward(self, in_types):
      type_check.expect(in_types.size() == 2)
      x_type, gy_type = in_types
      type_check.expect(x_type.dtype.kind == "f")
      type_check.expect(gy_type.dtype.kind == "f")
  
imports: import numpy
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the SoftplusGrad obj's underlying member method code with example usage: obj.check_type_forward(in_types) without return types