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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 dense_multitarget=True is supported for prediction explanations too, and shows just the top prediction highlighting.
Step1:  We can hide weights by passing force_weights=False (they still will be shown if it's impossible to highlight text)
Step2:  Show explanations for the winning class for first 10 documents from test data
Step3:  Now use a vectorizer that skips stopwords
Step4:  Words such as "the", "in", "of" are not used as features and are not highlighted
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Python Code:
show_html_expl(explain_prediction(clf, test['data'][2], vec, target_names=train['target_names']),
               force_weights=True)
Explanation: dense_multitarget=True is supported for prediction explanations too, and shows just the top prediction highlighting.
End of explanation
show_html_expl(explain_prediction(clf, test['data'][4], vec, target_names=train['target_names']), force_weights=False)
Explanation: We can hide weights by passing force_weights=False (they still will be shown if it's impossible to highlight text)
End of explanation
import numpy as np
for doc in test['data'][:10]:
    expl = explain_prediction(clf, doc, vec, target_names=train['target_names'], top_targets=1)
    show_html_expl(expl, force_weights=False)
Explanation: Show explanations for the winning class for first 10 documents from test data
End of explanation
vec_stop = TfidfVectorizer(stop_words='english')
clf_stop = LogisticRegressionCV()
pipeline_stop = Pipeline([('vec', vec_stop), ('clf', clf_stop)])
pipeline_stop.fit(train['data'], train['target'])
Explanation: Now use a vectorizer that skips stopwords
End of explanation
show_html_expl(explain_prediction(clf_stop, test['data'][4], vec_stop, target_names=train['target_names']), force_weights=False)
Explanation: Words such as "the", "in", "of" are not used as features and are not highlighted
End of explanation
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