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This error message...

Found the problem.
I was installing pandas_profiling, and this package updated pyyaml to version 6.0 which is not compatible with the current way Google Colab imports packages.
So just reverting back to pyyaml version 5.4.1 solved the problem.

For more information check versions of pyyaml here.
See this issue and formal answers in GitHub

##################################################################
For reverting back to pyyaml version 5.4.1 in your code, add the next line at the end of your packages installations:

Alright, until this problem get solved, I did this trick for my project:
I shared which files I need (like datasets) with my other accounts. For this, you should:

1. Go to your google drive (where your file is stored) then right-click on it and choose "Share"
2. Click on "Change to anyone with the link"
3. Copy link and open it in new window
4. In top-right side, click on your google accounts list and select which one you need
5. At the opened window, in top-right side click on "Add shortcut to Drive" and choose location where you want to save file in it
6. Your file now is accessible in account you did choose

Found this issue, pytube v11.0.1. It's a little late for me, but if no one has submitted a fix tomorrow I'll check it out.

in C:\Python38\lib\site-packages\pytube\parser.py

Change this line:

152: func_regex = re.compile(r"function\([^)]+\)")

to this:

152: func_regex = re.compile(r"function\([^)]?\)")

The issue is that the regex expects a function with an argument, but I guess youtube added some src that includes non-paramterized functions.

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

You could use django's receiver annotation.

For example, if you want to detect any call of the save method, you could do:

You're forgetting to loop over df_dict.items() instead of just df_dict ;)

Here, copying of data doesn't play a big role: the bottle neck is fast how the tanh-function is evaluated. There are many algorithms: some of them are faster some of them are slower, some are more precise some less.

Different numpy-distributions use different implementations of tanh-function, e.g. it could be one from mkl/vml or the one from the gnu-math-library.

Depending on numba version, also either the mkl/svml impelementation is used or gnu-math-library.

The easiest way to look inside is to use a profiler, for example perf.

For the numpy-version on my machine I get:

You need to upgrade the python to 3.9 and Pywin32 to Build 302. In addition, you need to install the OpenOPC-Python3x 1.3.1.