Bug: Simple pipeline with sklearn StandardScaler / LinearRegression does not work

[ActurialCapital]

Describe the bug
Simple pipeline with sklearn StandardScaler / LinearRegression does not work

To Reproduce

>>> from neuraxle.pipeline import Pipeline
>>> from neuraxle.steps.sklearn import SKLearnWrapper
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.linear_model import LinearRegression

>>> # Create your pipeline
>>> pipeline = Pipeline([
    SKLearnWrapper(StandardScaler()),  # Scale the features
    SKLearnWrapper(LinearRegression())  # Apply linear regression
])

>>> # Example data
>>> X = [[0, 1], [2, 3], [4, 5]]  # Features
>>> y = [0, 1, 2]  # Target variable

>>> # Fit the pipeline
>>> pipeline = pipeline.fit(X, y)
TypeError: LinearRegression.fit() missing 1 required positional argument: 'y'

Expected behavior
Preprocess and fit on train data

Additional context
neuraxle==0.8.1
scikit-learn = "^1.4.1"

[stale]

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