bkietz commented on code in PR #40365:
URL: https://github.com/apache/arrow/pull/40365#discussion_r1514860645
##########
cpp/src/arrow/record_batch_test.cc:
##########
@@ -705,17 +705,12 @@ TEST_F(TestRecordBatch, ToTensorSupportedNaN) {
std::vector<int64_t> shape = {9, 2};
const int64_t f32_size = sizeof(float);
std::vector<int64_t> f_strides = {f32_size, f32_size * shape[0]};
- std::vector<float> f_values = {
- static_cast<float>(NAN), 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40,
- static_cast<float>(NAN), 60, 70, 80, 90};
- auto data = Buffer::Wrap(f_values);
-
- std::shared_ptr<Tensor> tensor_expected;
- ASSERT_OK_AND_ASSIGN(tensor_expected, Tensor::Make(float32(), data, shape,
f_strides));
+ std::shared_ptr<Tensor> tensor_expected = TensorFromJSON(
+ float32(), shape,
+ "[NaN, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, NaN, 60, 70, 80, 90]",
f_strides);
Review Comment:
I'd say allowing for optional dimension permutations is the best way to
support non-row-major strides (my first thought for handling that was an enum
but I like the permutation idea better).
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