[MXNET-1029] Feature request: randint operator

[ChaiBapchya]

Description

Incorporates the feature request to support randint operator, includes optional tag across all random functions.
Fixes #11218

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Comments

• Flakiness of test_randint_generator
  This function tries to ensure the discrete uniform distribution is truly uniform.
  It uses the already defined verify_generator function but with default values, it failed the CI for certain config (specifically Python2 GPU.
  Ensured that I check in the above environment and verified the function works as mentioned in feedback.

MXNET_TEST_COUNT=10000 python -m nose -v --nocapture incubator-mxnet/tests/python/unittest/test_random.py:test_randint_generator --logging-level=DEBUG

[vrakesh]

@mxnet-label-bot [pr-awaiting-review]

[eric-haibin-lin]

Please create unit test (refer to test_random.py). Also please explicitly check edge cases like randint(dtype='int64', low=50000000, high=50000010)

comments addressed. good work

[ChaiBapchya]

@leezu over here I use rand_int64() by using mt19937.rand() for 2 32bits

According to the video : https://channel9.msdn.com/Events/GoingNative/2013/rand-Considered-Harmful
is this a right way of doing it truly uniform random integer distribution?

[leezu]

Yes, because you're concatenating 2 32bit numbers, where every bit is uniformly random. Thus the resulting 64bits are uniformly random.