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# Owner(s): ["module: dynamo"]
import pytest
from torch.testing._internal.common_utils import (
run_tests,
TEST_WITH_TORCHDYNAMO,
TestCase,
)
# If we are going to trace through these, we should use NumPy
# If testing on eager mode, we use torch._numpy
if TEST_WITH_TORCHDYNAMO:
import numpy as np
from numpy.testing import assert_equal
else:
import torch._numpy as np
from torch._numpy.testing import assert_equal
class TestAppend(TestCase):
# tests taken from np.append docstring
def test_basic(self):
result = np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
assert_equal(result, np.arange(1, 10, dtype=int))
# When `axis` is specified, `values` must have the correct shape.
result = np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
assert_equal(result, np.arange(1, 10, dtype=int).reshape((3, 3)))
with pytest.raises((RuntimeError, ValueError)):
np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
if __name__ == "__main__":
run_tests()
|
