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TypeError missing 1 required positional argument for pysrim package

I am trying to take advantage of the pysrim package to run some batch simulations of SRIM/TRIM in Jupyter notebook. I was able to successful install both the program and the package but am getting an error "TypeError: load() missing 1 required positional argument: 'Loader'" when using pysrim. Initially I assumed my Python version was to up to date (3.9.12) but am still getting this issue even after setting up a 3.6 environment. I understand this is a most likely a pretty niche question but any help would be much appreciated. Code pasted below.
import os
import numpy as np
import matplotlib.pyplot as plt
from srim import TRIM, Ion, Layer, Target
from srim.output import Results
Thanks!

Okay looks like I needed to change a small part of the program. I first navigated to C:\Users\<username>\Anaconda3\Lib\site-packages\srim\core and opened the file elementsdb.py. On line 10 I changed return yaml.load(open(dbpath, "r")) to
return yaml.full_load(open(dbpath, "r")) and the issue was resolved.

Imorting zero_gradients from torch.autograd.gradcheck

I want to replicate the code here, and I get the following error while running in Google Colab?
ImportError: cannot import name 'zero_gradients' from
'torch.autograd.gradcheck'
(/usr/local/lib/python3.7/dist-packages/torch/autograd/gradcheck.py)
Can someone help me with how to solve this?

This seems like it's using a very old version of PyTorch, the function itself is not available anymore. However, if you look at this commit, you will see the implementation of zero_gradients. What it does is simply zero out the gradient of the input:
def zero_gradients(i):
for t in iter_gradients(i):
t.zero_()
Then zero_gradients(x) should be the same as x.zero_grad(), which is the current API, assuming x is a nn.Module!
Or it's just:
if x.grad is not None:
x.grad.zero_()

'NearMiss' object has no attribute '_validate_data'

Detailed Image
This is the code below which shows the error.
from imblearn.under_sampling import NearMiss
nm = NearMiss()
X_res,y_res=nm.fit_sample(X,Y)

You are probably trying to under sample your imbalanced dataset. For this purpose, you can use RandomUnderSampler instead of NearMiss.
Try the following code:
from imblearn.under_sampling import RandomUnderSampler
under_sampler = RandomUnderSampler()
X_res, y_res = under_sampler.fit_resample(X, y)
Now, your dataset is balanced. You can verify it using y_res.value_counts().
Cheers!

Instead of "imblearn" package my conda installed a package named "imbalanced-learn" that's why it does not take the data. But it is strange that the jupyter notebook doesn't tell me that "imblearn" isn't installed.

No module named 'sklearn.utils.linear_assignment_'

I am trying to run a project from github , every object counter applications using sort algorithm. I can't run any of them because of a specific error, attaching errors screenshot. Can anyone help me about fixing this issue?

The linear_assignment function is deprecated in 0.21 and will be removed from 0.23, but sklearn.utils.linear_assignment_ can be replaced by scipy.optimize.linear_sum_assignment.
You can use:
from scipy.optimize import linear_sum_assignment as linear_assignment
then you can run the file and don't need to change the code.

pip install scikit-learn==0.22.2

As yiakwy points out in a github comment the scipy.optimize.linear_sum_assignment is not the perfect replacement:
I am concerned that linear_sum_assignment is not equivalent to linear_assignment which later implements "maximum values" matching strategy not "complete matching" strategy, i.e. in tracking problem maybe an old landmark lost and a new detection coming in. We don't have to make a complete assignment, just match as more as possible.
I have found this out while trying to use it inside SORT-based yolo tracking code which that replacement broke (I was lucky that it did otherwise, I would get wrong results from the experiments without realising it...)
Instead, I suggest copying the module itself to the last version of sklearn and include as module in your code.
https://github.com/scikit-learn/scikit-learn/blob/0.22.X/sklearn/utils/linear_assignment_.py
For instance if you copy this file into an utils directory import with from utils.linear_assignment_ import linear_assignment

Solution
Use pip to install lap and optionally scipy
Uncomment the import and use the following function
def linear_assignment(cost_matrix):
try:
import lap
_, x, y = lap.lapjv(cost_matrix, extend_cost=True)
return np.array([[y[i], i] for i in x if i >= 0])
except ImportError:
from scipy.optimize import linear_sum_assignment
x, y = linear_sum_assignment(cost_matrix)
return np.array(list(zip(x, y)))

You are getting this error because you haven't install scikit module yet.
Install scikit-learn module from https://pypi.org/project/scikit-learn/

Name 'RandomUnderSampler' is not defined

I'm trying to use RandomUnderSampler. I have correctly installed the imblearn module. But still getting the error: "Name 'RandomUnderSampler" is not defined`. Any specific reason for this? Can someone please help
from imblearn.under_sampling import RandomUnderSampler
#Random under-sampling and over-sampling with imbalanced-learn
def random_under_sampling(X,Y):
rus = RandomUnderSampler(return_indices=True)
X_rus, y_rus, id_rus = rus.fit_sample(X, Y)
print('Removed indexes:', id_rus)
plot_2d_space(X_rus, y_rus, 'Random under-sampling')
The actual method name
This is where I called my method

Since it seems that you are using IPython it is important that you execute first the line importing imblearn library (e.g. Ctrl-Enter):
from imblearn.under_sampling import RandomUnderSampler
After that the module should get imported and the name of the function is going to be defined.
If this does not work, could you reload the notebook and execute all the statements up until the random_under_sampling function to ensure nothing was missed?