API Error in Official Documentation

Documentation of linear algebra api provides makediag, maketrian, extractdiag and extracttrian APIs. But when I tried them it says:
AttributeError: module 'mxnet.ndarray.linalg' has no attribute 'makediag'
AttributeError: module 'mxnet.ndarray.linalg' has no attribute 'maketrian'
AttributeError: module 'mxnet.ndarray.linalg' has no attribute 'extractdiag'
AttributeError: module 'mxnet.ndarray.linalg' has no attribute 'extracttrian'
while rest of the provided APIs are working fine.
I am using mxnet version 1.4.1
Some more details of my system are below:

Version      : 3.6.8
Compiler     : MSC v.1916 64 bit (AMD64)
Build        : ('tags/v3.6.8:3c6b436a57', 'Dec 24 2018 00:16:47')
Arch         : ('64bit', 'WindowsPE')
------------Pip Info-----------
Version      : 19.1.1
Directory    : C:\Users\Rishik\AppData\Local\Programs\Python\Python36\lib\site-packages\pip
----------MXNet Info-----------
Version      : 1.4.1
Directory    : C:\Users\Rishik\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet
Hashtag not found. Not installed from pre-built package.
----------System Info----------
Platform     : Windows-10-10.0.17763-SP0
system       : Windows
node         : LAPTOP-KPGD1911
release      : 10
version      : 10.0.17763
----------Hardware Info----------
machine      : AMD64
processor    : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
Name                                      

Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz  



----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.2064 sec, LOAD: 2.7406 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.3039 sec, LOAD: 1.9452 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.5529 sec, LOAD: 2.1008 sec.
Error open FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)>, DNS finished in 0.5158889293670654 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0878 sec, LOAD: 4.4312 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.3054 sec, LOAD: 0.9500 sec.
[Finished in 25.6s]

Hi, you need to update to the latest version of mxnet, from my machine:

I’ve already upgraded using pip install --upgrade mxnet and got version 1.4.1
When I did pip install mxnet==1.5.0 it says ERROR: No matching distribution found for mxnet==1.5.0
Well I’m using windows, so I think its os related problem. BTW could you help me install the 1.5.0 version of mxnet? Because in documentation it says 1.4.1 is the latest version:
Capture

Master is the latest version. With pip you have two options:

  1. pip install mxnet --pre (assuming you don’t want cuda capabilities).
  2. type pip install mxnet== and press enter. This will give an error and print all available versions, from which you can choose. At present the nightly version is:
    (pip install mxnet== prints):
(base) dia021@bracewell-i1:~/isprs_latest/Review1/AblationStudy_6k/resuneta_cmtskc_hvd/CompareD6Full_vs_Simple/hvd_d7_global_mcc_large_lr> pip install mxnet==
Collecting mxnet==
  ERROR: Could not find a version that satisfies the requirement mxnet== (from versions: 0.9.5, 0.9.5.post2, 0.10.0, 0.10.0.post2, 0.11.0b20170816, 0.11.0, 0.11.1b20170823, 0.11.1b20170901, 0.11.1b20170908, 0.11.1b20170915, 0.11.1b20170922, 0.11.1b20170929, 0.11.1b20171006, 0.11.1b20171013, 0.12.0b20171020, 0.12.0b20171027, 0.12.0, 0.12.1b20171117, 0.12.1b20171124, 0.12.1b20171201, 0.12.1, 1.0.0b20171209, 1.0.0b20171216, 1.0.0, 1.0.0.post0, 1.0.0.post1, 1.0.0.post2, 1.0.0.post3, 1.0.0.post4, 1.0.1b20171224, 1.0.1b20171231, 1.0.1b20180107, 1.0.1b20180114, 1.0.1b20180121, 1.0.1b20180128, 1.0.1b20180202, 1.1.0b20180209, 1.1.0b20180216, 1.1.0, 1.1.0.post0, 1.2.0b20180223, 1.2.0b20180302, 1.2.0b20180309, 1.2.0b20180316, 1.2.0b20180323, 1.2.0b20180330, 1.2.0b20180406, 1.2.0b20180413, 1.2.0b20180420, 1.2.0b20180427, 1.2.0b20180504, 1.2.0b20180511, 1.2.0b20180518, 1.2.0b20180525, 1.2.0, 1.2.1, 1.2.1.post0, 1.2.1.post1, 1.3.0b20180601, 1.3.0b20180608, 1.3.0b20180614, 1.3.0b20180621, 1.3.0b20180712, 1.3.0b20180726, 1.3.0b20180802, 1.3.0b20180803, 1.3.0b20180805, 1.3.0b20180806, 1.3.0b20180807, 1.3.0b20180808, 1.3.0b20180809, 1.3.0b20180810, 1.3.0b20180811, 1.3.0b20180812, 1.3.0b20180814, 1.3.0b20180816, 1.3.0b20180817, 1.3.0b20180818, 1.3.0b20180819, 1.3.0b20180820, 1.3.0b20180821, 1.3.0b20180822, 1.3.0b20180823, 1.3.0b20180824, 1.3.0b20180825, 1.3.0b20180826, 1.3.0b20180827, 1.3.0b20180828, 1.3.0b20180829, 1.3.0b20180830, 1.3.0b20180831, 1.3.0b20180905, 1.3.0b20180906, 1.3.0b20180907, 1.3.0b20180908, 1.3.0b20180909, 1.3.0b20180911, 1.3.0b20180912, 1.3.0b20180914, 1.3.0b20180915, 1.3.0b20180916, 1.3.0, 1.3.0.post0, 1.3.1b20180918, 1.3.1b20180919, 1.3.1b20180920, 1.3.1b20180921, 1.3.1b20180922, 1.3.1b20180924, 1.3.1b20180925, 1.3.1b20180926, 1.3.1b20180927, 1.3.1b20180928, 1.3.1b20180929, 1.3.1b20180930, 1.3.1b20181001, 1.3.1b20181002, 1.3.1b20181003, 1.3.1b20181004, 1.3.1b20181009, 1.3.1b20181010, 1.3.1b20181011, 1.3.1b20181012, 1.3.1b20181013, 1.3.1b20181014, 1.3.1b20181015, 1.3.1b20181017, 1.3.1b20181018, 1.3.1b20181019, 1.3.1b20181020, 1.3.1b20181021, 1.3.1b20181022, 1.3.1b20181023, 1.3.1b20181024, 1.3.1b20181027, 1.3.1b20181028, 1.3.1b20181029, 1.3.1b20181030, 1.3.1b20181031, 1.3.1b20181101, 1.3.1b20181103, 1.3.1b20181104, 1.3.1b20181105, 1.3.1b20181106, 1.3.1b20181107, 1.3.1b20181108, 1.3.1b20181109, 1.3.1b20181110, 1.3.1b20181111, 1.3.1b20181114, 1.3.1b20181115, 1.3.1b20181116, 1.3.1b20181117, 1.3.1b20181118, 1.3.1b20181119, 1.3.1b20181120, 1.3.1, 1.4.0b20181121, 1.4.0b20181122, 1.4.0b20181123, 1.4.0b20181124, 1.4.0b20181125, 1.4.0b20181126, 1.4.0b20181128, 1.4.0b20181129, 1.4.0b20181130, 1.4.0b20181201, 1.4.0b20181202, 1.4.0b20181203, 1.4.0b20181204, 1.4.0b20181206, 1.4.0, 1.4.0.post0, 1.4.1, 1.5.0b20181205, 1.5.0b20181207, 1.5.0b20181208, 1.5.0b20181209, 1.5.0b20181210, 1.5.0b20181211, 1.5.0b20181212, 1.5.0b20181213, 1.5.0b20181214, 1.5.0b20181216, 1.5.0b20181217, 1.5.0b20181218, 1.5.0b20181219, 1.5.0b20181220, 1.5.0b20181221, 1.5.0b20181222, 1.5.0b20181223, 1.5.0b20181224, 1.5.0b20181225, 1.5.0b20181226, 1.5.0b20181227, 1.5.0b20181228, 1.5.0b20181229, 1.5.0b20181230, 1.5.0b20181231, 1.5.0b20190101, 1.5.0b20190102, 1.5.0b20190106, 1.5.0b20190107, 1.5.0b20190110, 1.5.0b20190112, 1.5.0b20190115, 1.5.0b20190116, 1.5.0b20190117, 1.5.0b20190118, 1.5.0b20190119, 1.5.0b20190120, 1.5.0b20190121, 1.5.0b20190122, 1.5.0b20190123, 1.5.0b20190124, 1.5.0b20190125, 1.5.0b20190126, 1.5.0b20190127, 1.5.0b20190128, 1.5.0b20190130, 1.5.0b20190131, 1.5.0b20190201, 1.5.0b20190202, 1.5.0b20190203, 1.5.0b20190204, 1.5.0b20190205, 1.5.0b20190206, 1.5.0b20190207, 1.5.0b20190208, 1.5.0b20190209, 1.5.0b20190210, 1.5.0b20190211, 1.5.0b20190212, 1.5.0b20190214, 1.5.0b20190215, 1.5.0b20190216, 1.5.0b20190217, 1.5.0b20190218, 1.5.0b20190219, 1.5.0b20190220, 1.5.0b20190221, 1.5.0b20190222, 1.5.0b20190223, 1.5.0b20190224, 1.5.0b20190225, 1.5.0b20190226, 1.5.0b20190227, 1.5.0b20190228, 1.5.0b20190301, 1.5.0b20190302, 1.5.0b20190303, 1.5.0b20190304, 1.5.0b20190305, 1.5.0b20190308, 1.5.0b20190309, 1.5.0b20190310, 1.5.0b20190311, 1.5.0b20190312, 1.5.0b20190313, 1.5.0b20190314, 1.5.0b20190325, 1.5.0b20190326, 1.5.0b20190328, 1.5.0b20190329, 1.5.0b20190330, 1.5.0b20190331, 1.5.0b20190401, 1.5.0b20190402, 1.5.0b20190403, 1.5.0b20190404, 1.5.0b20190405, 1.5.0b20190406, 1.5.0b20190407, 1.5.0b20190408, 1.5.0b20190409, 1.5.0b20190410, 1.5.0b20190411, 1.5.0b20190412, 1.5.0b20190425, 1.5.0b20190426, 1.5.0b20190427, 1.5.0b20190428, 1.5.0b20190429, 1.5.0b20190430, 1.5.0b20190501, 1.5.0b20190502, 1.5.0b20190503, 1.5.0b20190504, 1.5.0b20190505, 1.5.0b20190506, 1.5.0b20190507, 1.5.0b20190508, 1.5.0b20190509, 1.5.0b20190510, 1.5.0b20190511, 1.5.0b20190512, 1.5.0b20190513, 1.5.0b20190514, 1.5.0b20190515, 1.5.0b20190516, 1.5.0b20190517, 1.5.0b20190518, 1.5.0b20190519, 1.5.0b20190520, 1.5.0b20190521, 1.5.0b20190523, 1.5.0b20190525, 1.5.0b20190526, 1.5.0b20190527, 1.5.0b20190528, 1.5.0b20190529, 1.5.0b20190530, 1.5.0b20190531, 1.5.0b20190602, 1.5.0b20190603, 1.5.0b20190604, 1.5.0b20190605, 1.5.0b20190606)

so you install (today) the latest version with: pip install mxnet==1.5.0b20190606

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Thanks a gazillion times.

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