add ctx argument for rand_ndarray and rand_sparse_ndarray test util funcs

[haojin2]

Description

As title. Improve usability of those 2 test utilities functions. Previously one has to do something like:

a = rand_ndarray(shape, stype, density).as_in_context(ctx)

With this change one only need to:

a = rand_ndarray(shape, stype, density, ctx)

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

Changes

• Add ctx argument for rand_ndarray and rand_sparse_ndarray functions

Comments

Don't think this needs a dedicated unit test, but still got one small script in place to verify correctness of the change:

import mxnet as mx
from mxnet.test_utils import rand_ndarray

shape = (1000, 1000)
a = rand_ndarray(shape, stype='default')
print(a.context)
b = rand_ndarray(shape, stype='csr', density=0.01)
print(b.context)
c = rand_ndarray(shape, stype='row_sparse', density=0.01)
print(c.context)
ctx = mx.gpu(1)
a = rand_ndarray(shape, stype='default', ctx=ctx)
print(a.context)
b = rand_ndarray(shape, stype='csr', density=0.01, ctx=ctx)
print(b.context)
c = rand_ndarray(shape, stype='row_sparse', density=0.01, ctx=ctx)
print(c.context)

Result of running this script:

cpu(0)
cpu(0)
cpu(0)
gpu(1)
gpu(1)
gpu(1)