rshyamsundar opened a new issue #16407: Output of sampling method always has 
float32 type irrespective of the type of the parameters
URL: https://github.com/apache/incubator-mxnet/issues/16407
 
 
   ## Description
   Output of sampling method for various distributions (normal, gamma, etc.) 
always has `np.float32` type irrespective of the type of the parameters passed. 
Ideally the returned samples should have the same `dtype` as the parameters 
passed.
   
   ## Environment info (Required)
   
   ```
   ----------Python Info----------
   Version      : 3.6.5
   Compiler     : GCC 4.2.1 Compatible Apple LLVM 9.1.0 (clang-902.0.39.2)
   Build        : ('default', 'Jun 17 2018 12:13:06')
   Arch         : ('64bit', '')
   ------------Pip Info-----------
   Version      : 19.2.1
   Directory    : /usr/local/lib/python3.6/site-packages/pip
   ----------MXNet Info-----------
   Version      : 1.5.1
   Directory    : /usr/local/lib/python3.6/site-packages/mxnet
   Commit Hash   : c9818480680f84daa6e281a974ab263691302ba8
   Library      : ['/usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so']
   Build features:
   ✖ CUDA
   ✖ CUDNN
   ✖ NCCL
   ✖ CUDA_RTC
   ✖ TENSORRT
   ✔ CPU_SSE
   ✔ CPU_SSE2
   ✔ CPU_SSE3
   ✔ CPU_SSE4_1
   ✔ CPU_SSE4_2
   ✖ CPU_SSE4A
   ✔ CPU_AVX
   ✖ CPU_AVX2
   ✖ OPENMP
   ✖ SSE
   ✖ F16C
   ✖ JEMALLOC
   ✖ BLAS_OPEN
   ✖ BLAS_ATLAS
   ✖ BLAS_MKL
   ✖ BLAS_APPLE
   ✔ LAPACK
   ✖ MKLDNN
   ✔ OPENCV
   ✖ CAFFE
   ✖ PROFILER
   ✔ DIST_KVSTORE
   ✖ CXX14
   ✖ INT64_TENSOR_SIZE
   ✔ SIGNAL_HANDLER
   ✖ DEBUG
   ----------System Info----------
   Platform     : Darwin-17.7.0-x86_64-i386-64bit
   system       : Darwin
   node         : 4c327598c595.ant.amazon.com
   release      : 17.7.0
   version      : Darwin Kernel Version 17.7.0: Sun Jun  2 20:31:42 PDT 2019; 
root:xnu-4570.71.46~1/RELEASE_X86_64
   ----------Hardware Info----------
   machine      : x86_64
   processor    : i386
   b'machdep.cpu.brand_string: Intel(R) Core(TM) i7-5557U CPU @ 3.10GHz'
   b'machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE 
MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 PCLMULQDQ 
DTES64 MON DSCPL VMX EST TM2 SSSE3 FMA CX16 TPR PDCM SSE4.1 SSE4.2 x2APIC MOVBE 
POPCNT AES PCID XSAVE OSXSAVE SEGLIM64 TSCTMR AVX1.0 RDRAND F16C'
   b'machdep.cpu.leaf7_features: SMEP ERMS RDWRFSGS TSC_THREAD_OFFSET BMI1 AVX2 
BMI2 INVPCID SMAP RDSEED ADX IPT FPU_CSDS MD_CLEAR IBRS STIBP L1DF SSBD'
   b'machdep.cpu.extfeatures: SYSCALL XD 1GBPAGE EM64T LAHF LZCNT PREFETCHW 
RDTSCP TSCI'
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.1143 
sec, LOAD: 0.9243 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1232 sec, LOAD: 
1.0671 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1203 sec, LOAD: 
0.7703 sec.
   Timing for FashionMNIST: 
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
 DNS: 0.1115 sec, LOAD: 0.5783 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.1102 sec, LOAD: 
1.1630 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.1120 sec, 
LOAD: 0.1953 sec.
   ----------Environment----------
   KMP_DUPLICATE_LIB_OK="True"
   ```
   
   Package used (Python/R/Scala/Julia):
   I'm using Python 3.6.5
   
   ## Build info (Required if built from source)
   
   Compiler (gcc/clang/mingw/visual studio):
   
   MXNet commit hash:
   c981848
   (version: 1.5.1)
   
   ## Minimum reproducible example
   ```
   In [1]: import mxnet as mx                                                   
                                                                                
          
   
   In [2]: import numpy as np                                                   
                                                                                
          
   
   In [3]: mu = mx.nd.array([1], dtype=np.float64)                              
                                                                                
          
   
   In [4]: sigma = mx.nd.array([0.1], dtype=np.float64)                         
                                                                                
          
   
   In [5]: sample = mx.nd.sample_normal(mu, sigma)                              
                                                                                
          
   
   In [6]: mu.dtype, sigma.dtype, sample.dtype                                  
                                                                                
          
   Out[6]: (numpy.float64, numpy.float64, numpy.float32)
   
   ```

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