Use self->status in EagerTensor_numpy and EagerTensor_getbuffer
There is no reason to create a new status on each call. PiperOrigin-RevId: 257594441
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@ -657,10 +657,11 @@ static PyObject* EagerTensor_copy_to_device(EagerTensor* self, PyObject* args,
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// other.
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// Note that if `self` is not on CPU, we raise an Exception.
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static PyObject* EagerTensor_numpy(EagerTensor* self) {
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auto status = tensorflow::make_safe(TF_NewStatus());
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auto* py_array = TFE_TensorHandleToNumpy(self->handle, status.get());
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if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) {
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auto* py_array = TFE_TensorHandleToNumpy(self->handle, self->status);
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if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) {
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Py_XDECREF(py_array);
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// Cleanup self->status before returning.
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TF_SetStatus(self->status, TF_OK, "");
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return nullptr;
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} else {
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return PyArray_Return(reinterpret_cast<PyArrayObject*>(py_array));
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@ -745,13 +746,14 @@ static int EagerTensor_getbuffer(EagerTensor* self, Py_buffer* view,
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return -1;
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}
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auto status = tensorflow::make_safe(TF_NewStatus());
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// TensorHandleToNumpy is zero-copy for everything but DT_RESOURCE and
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// DT_STRING so the following is only slightly slower than a NumPy-free
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// implementation.
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auto py_array = tensorflow::make_safe(
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TFE_TensorHandleToNumpy(self->handle, status.get()));
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if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_BufferError)) {
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TFE_TensorHandleToNumpy(self->handle, self->status));
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if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_BufferError)) {
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// Cleanup self->status before returning.
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TF_SetStatus(self->status, TF_OK, "");
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return -1;
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}
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if (PyObject_GetBuffer(py_array.get(), view, flags) < 0) {
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