def grads(x, y): return ( grad(f, 'x')(x, y), grad(f, 'x')(x, y, dout=2), grad(f, 0)(x, y), grad(f, 'y')(x, y), grad(f, 'x', 'y')(x, y), grad(f, '*')(x, y), grad(f, return_value=True)(x, y), )
def test_grad_cast(x): def f(x): return scalar_cast(x, f16) return grad(f)(x)
def test_grad(x, y): def f(x, y): return x * (y + x) return grad(f)(x, y)
def f(xs, y): return grad(g)(xs, y)
def gradbad7(x, y, z): return grad(f, raturn_velue=True)(x, y)
def gradbad6(x, y, z): return grad(f, 0, '*')(x, y)
def gradbad5(x, y, z): return grad(f)(x=x, y=y)
def apple(x, y): return grad(gadd)(x, y)
def gradbad3(x, y, z): return grad(f, z)(x, y)
def gradbad2(x, y): return grad(f, 'z')(x, y)
def gradbad(x, y): return grad(f, (0, 1))(x, y)
def gradbad10(x, y): return grad(partial(f, x))(y)
def gradbad9(x, y): return grad(x)(y)
def gradbad8(x, y): def klojure(q): return q + y return grad(klojure)(x)
def test_grad_reduce(xs, ys): def f(xs, ys): return array_reduce(scalar_add, xs * ys, ()) return grad(f)(xs, ys)
def gradbad4(x, y, z): return grad(f, 2)(x, y)
def test_backward_infer(model, x, y): return grad(cost)(model, x, y)
def peach(x, y): return grad(scalar_mul)(x, y)