code: def backward(self, indexes, grad_outputs):
      if self.x_zero.any():
          warnings.warn(
              "cannot calculate gradient for zero input.",
              RuntimeWarning,
          )
      (gy,) = grad_outputs
      dx = (1 + self._logx) * (1 - self.x_zero)
      return (gy * dx,)
  
file_path: chainer/chainer/distributions/utils.py
imports: import warnings
  import chainer
  from chainer.functions.array import where
  from chainer.functions.math import exponential
  from chainer import utils
label_desc: Use image node_id 3 for calling the ModifiedXLogX obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: unknown