PR NVIDIA/cub#218 fixes this CUB's radix sort. We should:

• Check whether Thrust's other backends handle this case correctly.
• Provide a guarantee of this in the stable_sort documentation.
• Add regression tests to enforce this on all backends.

confusion_matrix should automatically convert dtypes as appropriate in order to avoid failing, like other metric functions.

from sklearn.metrics import confusion_matrix
import numpy as np
import cuml
​
y = np.array([0.0, 1.0, 0.0])
y_pred = np.array([0.0, 1.0, 1.0])
print(confusion_matrix(y, y_pred))
cuml.metrics.confusion_matrix(y, y_pred)
[[1 1]
 [0 1]]
----------------

Segmented reduce uses the same template type OffsetIteratorT for begin and end offsets

static CUB_RUNTIME_FUNCTION cudaError_t cub::DeviceSegmentedReduce::Sum
    (   void *              d_temp_storage,
        size_t &            temp_storage_bytes,
        InputIteratorT      d_in,
        OutputIteratorT     d_out,
        int                 num_segments,
        OffsetIterato

Add a section to _compiler.py that wraps ScriptFunctions in Modules to compile the graph.