class_docstr: Computes the gradient of the ReLU function.
  
  This function takes 2 variables b and c, and
  computes f(b, c) = sign(b) * c with backpropagation
  where operations are done in elementwise manner
  and sign(x) = 1 when x > 0 is positive and 0 otherwise.
  
  As the gradient of f with respect to b is 0,
  we do not backpropagate errors toward b for computational efficiency.
class_docstr_tok: ["Computes","the","gradient","of","the","ReLU","function",".","This","function","takes","2","variables","b","and","c",",","and","computes","f","(","b",",","c",")","=","sign","(","b",")","*","c","with","backpropagation","where","operations","are","done","in","elementwise","manner","and","sign","(","x",")","=","1","when","x",">","0","is","positive","and","0","otherwise",".","As","the","gradient","of","f","with","respect","to","b","is","0",",","we","do","not","backpropagate","errors","toward","b","for","computational","efficiency","."]
code: def backward(self, indexes, grad_outputs):
      return ReLUGrad2(self.b).apply(grad_outputs)
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 5 for calling the ReLUGrad2 obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ReLUGrad2