'Gives the (sub/super)gradient of the atom w.r.t. each argument.
Matrix expressions are vectorized, so the gradient is a matrix.
Args:
values: A list of numeric values for the arguments.
Returns:
A list of SciPy CSC sparse matrices or None.'

def _grad(self, values):

    rows = (self.args[0].size[0] * self.args[0].size[1])
    cols = (self.size[0] * self.size[1])
    grad_vals = np.exp(values[0])
    return [exp.elemwise_grad_to_diag(grad_vals, rows, cols)]
