detach().cpu() kills kernel
Background
I am trying to plot an image noise using pytorch, however, when I reach to that point, the kernel dies. I am attempting the same code at Google Colab where I do get results
Result at Google Colab

Result at Jupyter

I do not think that it has something to do with the code itself, but I am posting the function to plot the grid:
def Exec_ShowImgGrid(ObjTensor, NumCh=1, NumSizeData=(28,28), NumImgs=16):
    #tensor: 128(pictures at the time ) * 784 (28*28)
    Objdata= ObjTensor.detach().cpu().view(-1,NumCh,*NumSizeData) #128 *1 *28*28 
    Objgrid= make_grid(Objdata[:NumCh],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
    Objpyplot.imshow(Objgrid)
    Objpyplot.show()

I set a pdb and I can notice it does not run the Objdata line, so I am assuming it has to do with the detach().cpu()
These are the libraries from the environment used, which I think may be the culprit
name: GPUBase
channels:
  - pytorch
  - defaults
dependencies:
  - argon2-cffi=21.3.0=pyhd3eb1b0_0
  - argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
  - async_generator=1.10=pyhd3eb1b0_0
  - attrs=21.4.0=pyhd3eb1b0_0
  - backcall=0.2.0=pyhd3eb1b0_0
  - blas=1.0=mkl
  - bleach=4.1.0=pyhd3eb1b0_0
  - ca-certificates=2021.10.26=haa95532_4
  - certifi=2021.10.8=py39haa95532_2
  - cffi=1.15.0=py39h2bbff1b_1
  - colorama=0.4.4=pyhd3eb1b0_0
  - cpuonly=2.0=0
  - cudatoolkit=11.3.1=h59b6b97_2
  - debugpy=1.5.1=py39hd77b12b_0
  - decorator=5.1.1=pyhd3eb1b0_0
  - defusedxml=0.7.1=pyhd3eb1b0_0
  - entrypoints=0.3=py39haa95532_0
  - freetype=2.10.4=hd328e21_0
  - importlib_metadata=4.8.2=hd3eb1b0_0
  - intel-openmp=2021.4.0=haa95532_3556
  - ipykernel=6.4.1=py39haa95532_1
  - ipython=7.31.1=py39haa95532_0
  - ipython_genutils=0.2.0=pyhd3eb1b0_1
  - jedi=0.18.1=py39haa95532_1
  - jinja2=3.0.2=pyhd3eb1b0_0
  - jpeg=9d=h2bbff1b_0
  - jsonschema=3.2.0=pyhd3eb1b0_2
  - jupyter_client=7.1.2=pyhd3eb1b0_0
  - jupyter_core=4.9.1=py39haa95532_0
  - jupyterlab_pygments=0.1.2=py_0
  - libpng=1.6.37=h2a8f88b_0
  - libtiff=4.2.0=hd0e1b90_0
  - libuv=1.40.0=he774522_0
  - libwebp=1.2.0=h2bbff1b_0
  - lz4-c=1.9.3=h2bbff1b_1
  - markupsafe=2.0.1=py39h2bbff1b_0
  - matplotlib-inline=0.1.2=pyhd3eb1b0_2
  - mistune=0.8.4=py39h2bbff1b_1000
  - mkl=2021.4.0=haa95532_640
  - mkl-service=2.4.0=py39h2bbff1b_0
  - mkl_fft=1.3.1=py39h277e83a_0
  - mkl_random=1.2.2=py39hf11a4ad_0
  - nbclient=0.5.3=pyhd3eb1b0_0
  - nbconvert=6.1.0=py39haa95532_0
  - nbformat=5.1.3=pyhd3eb1b0_0
  - nest-asyncio=1.5.1=pyhd3eb1b0_0
  - notebook=6.4.8=py39haa95532_0
  - numpy-base=1.21.5=py39hc2deb75_0
  - olefile=0.46=pyhd3eb1b0_0
  - openssl=1.1.1m=h2bbff1b_0
  - packaging=21.3=pyhd3eb1b0_0
  - pandocfilters=1.5.0=pyhd3eb1b0_0
  - parso=0.8.3=pyhd3eb1b0_0
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pip=21.2.4=py39haa95532_0
  - prometheus_client=0.13.1=pyhd3eb1b0_0
  - prompt-toolkit=3.0.20=pyhd3eb1b0_0
  - pycparser=2.21=pyhd3eb1b0_0
  - pygments=2.11.2=pyhd3eb1b0_0
  - pyrsistent=0.18.0=py39h196d8e1_0
  - python=3.9.7=h6244533_1
  - python-dateutil=2.8.2=pyhd3eb1b0_0
  - pytorch-mutex=1.0=cpu
  - pywin32=302=py39h827c3e9_1
  - pywinpty=2.0.2=py39h5da7b33_0
  - pyzmq=22.3.0=py39hd77b12b_2
  - send2trash=1.8.0=pyhd3eb1b0_1
  - setuptools=58.0.4=py39haa95532_0
  - six=1.16.0=pyhd3eb1b0_1
  - sqlite=3.37.2=h2bbff1b_0
  - terminado=0.13.1=py39haa95532_0
  - testpath=0.5.0=pyhd3eb1b0_0
  - tk=8.6.11=h2bbff1b_0
  - tornado=6.1=py39h2bbff1b_0
  - traitlets=5.1.1=pyhd3eb1b0_0
  - typing_extensions=3.10.0.2=pyh06a4308_0
  - tzdata=2021e=hda174b7_0
  - vc=14.2=h21ff451_1
  - vs2015_runtime=14.27.29016=h5e58377_2
  - wcwidth=0.2.5=pyhd3eb1b0_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - wincertstore=0.2=py39haa95532_2
  - winpty=0.4.3=4
  - xz=5.2.5=h62dcd97_0
  - zipp=3.7.0=pyhd3eb1b0_0
  - zlib=1.2.11=h8cc25b3_4
  - zstd=1.4.9=h19a0ad4_0
  - pip:
    - absl-py==1.0.0
    - astunparse==1.6.3
    - cachetools==5.0.0
    - charset-normalizer==2.0.12
    - cycler==0.11.0
    - docutils==0.18.1
    - flatbuffers==2.0
    - fonttools==4.29.1
    - gast==0.5.3
    - google-auth==2.6.0
    - google-auth-oauthlib==0.4.6
    - google-pasta==0.2.0
    - grpcio==1.44.0
    - h5py==3.6.0
    - htmlmin==0.1.12
    - idna==3.3
    - imagehash==4.2.1
    - importlib-metadata==4.11.1
    - ipywidgets==7.6.5
    - joblib==1.0.1
    - jupyterlab-widgets==1.0.2
    - keras==2.8.0
    - keras-preprocessing==1.1.2
    - keyring==23.5.0
    - kiwisolver==1.3.2
    - libclang==13.0.0
    - markdown==3.3.6
    - matplot==0.1.9
    - matplotlib==3.5.1
    - missingno==0.5.0
    - multimethod==1.7
    - networkx==2.6.3
    - numpy==1.22.2
    - oauthlib==3.2.0
    - opt-einsum==3.3.0
    - pandas==1.4.1
    - pandas-profiling==3.1.0
    - phik==0.12.0
    - pillow==9.0.1
    - pkginfo==1.8.2
    - protobuf==3.19.4
    - pyasn1==0.4.8
    - pyasn1-modules==0.2.8
    - pydantic==1.9.0
    - pyloco==0.0.139
    - pyparsing==3.0.7
    - pytz==2021.3
    - pywavelets==1.2.0
    - pywin32-ctypes==0.2.0
    - pyyaml==6.0
    - readme-renderer==32.0
    - requests==2.27.1
    - requests-oauthlib==1.3.1
    - requests-toolbelt==0.9.1
    - rfc3986==2.0.0
    - rsa==4.8
    - scikit-learn==1.0.2
    - scipy==1.8.0
    - seaborn==0.11.2
    - simplewebsocketserver==0.1.1
    - tangled-up-in-unicode==0.1.0
    - tensorboard==2.8.0
    - tensorboard-data-server==0.6.1
    - tensorboard-plugin-wit==1.8.1
    - tensorflow==2.8.0
    - tensorflow-gpu==2.8.0
    - tensorflow-io-gcs-filesystem==0.24.0
    - termcolor==1.1.0
    - tf-estimator-nightly==2.8.0.dev2021122109
    - threadpoolctl==3.1.0
    - torch==1.10.2
    - torchaudio==0.7.2
    - torchutils==0.0.4
    - torchvision==0.8.2+cu110
    - tqdm==4.62.3
    - twine==3.8.0
    - typing==3.7.4.3
    - typing-extensions==4.1.1
    - urllib3==1.26.8
    - ushlex==0.99.1
    - visions==0.7.4
    - webencodings==0.5.1
    - websocket-client==1.2.3
    - werkzeug==2.0.3
    - widgetsnbextension==3.5.2
    - wrapt==1.13.3
    - xlwings==0.26.3

Question
How can I be able to plot as in google colab for the function stated?

After a few days I was able to find the solution
Firstly, my code needed to be fixed to correctly call the params needed with the proper name
def Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
    #tensor: 128(pictures at the time ) * 784 (28*28)
    Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28 
    Objgrid= make_grid(Objdata[:num],nrow=4).permute(1,2,0) #1*28*28 = 28*28*1 #Mathplot library isnt the saame as pytorch, we are accomodating the args
    Objpyplot.imshow(Objgrid)

    Objpyplot.show()

The Kernel was dying still, the only solution was to install numba in the environment as this answer states. I only had to append the from numba import cuda statement at my declarations.
