[dynamo] Fix numpy test accuracy error induced by randomness divergence (#145293)

Previously `TestGradient.test_second_order_accurate` was failing because
of a small tolerance error (0.03... which is above the 0.03 tolerance).

Upon investigating, `np.random.random` caused some divergence between
eager and compiled randomness because in compiled we are not using
`np.random`'s random seed, rather we end up using `torch`'s. This in
turn caused numerical divergence and aforementioned accuracy issue.

This patch fixes the failure by patching the test case with
`use_numpy_random_stream=True`, which forces a graph break on
`np.random.random()` and thereby falling back to eager to ensure
consistency of the numpy randomness.

Fixes #116746.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145293
Approved by: https://github.com/lezcano

test/torch_np/numpy_tests/lib/test_function_base.py

@@ -16,6 +16,7 @@ import pytest
 from hypothesis.extra.numpy import arrays
 from pytest import raises as assert_raises
 
+import torch
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
     parametrize,
@@ -1013,6 +1014,7 @@ class TestGradient(TestCase):
         assert_raises(TypeError, gradient, f_2d, x, x, axis=1)
         assert_raises(TypeError, gradient, f_2d, 1, 1, axis=1)
 
+    @torch._dynamo.config.patch(use_numpy_random_stream=True)
     def test_second_order_accurate(self):
         # Testing that the relative numerical error is less that 3% for
         # this example problem. This corresponds to second order