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cannot import name 'set_policy' from 'keras.mixed_precision.policy'

when i try to do "from tensorflow.keras import intitializers" i get
"cannot import name 'set_policy' from 'keras.mixed_precision.policy'" this error.
Any solutions?

I would try:
tf.keras.mixed_precision.set_global_policy
or
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_global_policy(policy)
More info you can find here:
https://www.tensorflow.org/guide/mixed_precision

go to the error directory and change the import from
from tensorflow.python.keras.mixed_precision.policy import set_policy
to
from tensorflow.python.keras.mixed_precision.policy import set_global_policy
or
from tensorflow.python.keras.mixed_precision.policy import set_policy as set_global_policy

ImportError: cannot import name 'SegmentationMapsOnImage' from 'imgaug.augmentables.segmaps'

When i import it, i get an error about that.
from imgaug.augmentables.segmaps import SegmentationMapsOnImage
Error:
ImportError: cannot import name 'SegmentationMapsOnImage' from
'imgaug.augmentables.segmaps'
(/usr/local/lib/python3.7/dist-packages/imgaug/augmentables/segmaps.py)
Here is the documentation link
Thanks in advance!

How to add a function into utils module on titanic data set?

When I run the following code. I am having an error saying AttributeError: module 'python_utils' has no attribute 'clean_data', but I know know how to fix it.
import python_utils
import pandas as pd
from sklearn import linear_model
def clean_data(data):
data["Fare"]=data["Fare"].fillna(data["Fare"].dropna().median())
data["Age"]=data["Age"].fillna(data["age"].dropna().median())
data.loc[data["Sex"]=="male", "Sex"]=0
data.loc[data["Sex"]=="female", "Sex"]=1
data["Embarked"]=data["Embarked"].fillna("S")
data.loc[data["Embarked"]=="S",Embarked]=0
data.loc[data["Embarked"]=="C",Embarked]=1
data.loc[data["Embarked"]=="Q",Embarked]=2
train=pd.read_csv('train.csv')
python_utils.clean_data(train)
target=train["Survived"].values
features=train[["Pclass","Age","Sex","SibSp","Parch"]].values
classifier=linear_model.logisticRegression()
classifier=classifier.fit(features,target)
print(classifier_.score(features,target))

Not able to create shortcut for sklearn

I entered the following code:
import sklearn
import sklearn as sk
import sklearn.preprocessing as skl
from sklearn.preprocessing import Imputer
from sk.preprocessing import Imputer
from skl import Imputer
The part which reads; from sklearn.preprocessing import Imputer gets executed normally.
However, when I run from sk.preprocessing import Imputer, I get the following error:
from sk.preprocessing import Imputer
Traceback (most recent call last):`
File "<ipython-input-84-fc12144914d1>", line 1, in <module>`
from sk.preprocessing import Imputer`
ModuleNotFoundError: No module named 'sk'`
And from skl import Imputer yields the following:
from skl import Imputer`
Traceback (most recent call last):`
File "<ipython-input-85-1e925587d122>", line 1, in <module>`
from skl import Imputer`
ModuleNotFoundError: No module named 'skl'`
Why am I not able to create a shortcut for the Library?

Because it is wrong to do so. The right way do do it is as you have written already.
from sklearn.preprocessing import Imputer
the __init__.py in the preprocessing directory of sklearn defines the possible imports from that level.
The below is a valid aliasing and i think is what you are looking for.
from sklearn.preprocessing import Imputer as imp

ImportError: cannot import name VarianceThreshold

scikit-learn seems to work, but when I did:
from sklearn.feature_selection import VarianceThreshold
I got the following error:
ImportError: cannot import name VarianceThreshold
How to bypass this? I am a newbie in Python, so I have no idea what to do.
I played with the order of my imports, as suggested here: ImportError: Cannot import name X, but no luck.
import sys
import pandas as pd
import numpy as np
import operator
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import normalize
from sklearn import decomposition
I am also getting this:
code/python/k_means/serial_version$ python -c 'import sklearn; print(sklearn.VarianceThreshold)'
Traceback (most recent call last):
File "<string>", line 1, in <module>
AttributeError: 'module' object has no attribute 'VarianceThreshold'
Version:
>>> import sklearn
>>> sklearn.__version__
'0.14.1'

You can bypass by catching the exception
try:
from sklearn.feature_selection import VarianceThreshold
except:
pass # it will catch any exception here
If you want to catch only Attribue Error Exception then use below
try:
from sklearn.feature_selection import VarianceThreshold
except AttributeError:
pass # catches only Attribute Exception