Python - window size for machine learning model

Question

i am working on a python task using logistic regression classifier and i am trying to set w window size = 2 for the input data before the fitting step. here is what i have tried

from itertools import islice
def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result
    for elem in it:
        result = result[1:] + (elem,)
        yield result

x_train = list(window(x_train))
y_train = list(window(y_train))
x_test = list(window(x_test))
y_test = list(window(y_test))
seed = 42
##LogisticRegressionCV Classifier
lr = LogisticRegression()
lr.fit(x_train,y_train)
y_pred1=lr.predict(x_test)

kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(lr, x_train, y_train, cv=kfold)

here is i have used a function to apply a window size = 2 but in the fitting step the following error appears because the shape of the dataset after the windowing is edited for example like that ((1150731, 2, 3)) instead of (1150731,3)

ValueError: Found array with dim 3. Estimator expected <= 2.