Expected 2D array got 1D array instead array

Question

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression(normalize = True)
display_model_performance("Linear Regression",lin_reg)

ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_27260/2220606045.py in <module>
      2 
      3 lin_reg = LinearRegression(normalize = True)
----> 4 display_model_performance("Linear Regression",lin_reg)
      5 

~\AppData\Local\Temp/ipykernel_27260/2396785647.py in display_model_performance(model_name, model, diamonds, labels, models_rmse, cvs_rmse_mean, tests_rmse, tests_accuracy, pipeline, x_test, y_test, cv)
     44     print("--- Test Performance ---")
     45 
---> 46     x_test_prepared = pipeline.transform(x_test)
     47 
     48      # Fit test dataset in model

C:\Users\Public\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in transform(self, X)
    717         """
    718         check_is_fitted(self)
--> 719         X = _check_X(X)
    720 
    721         fit_dataframe_and_transform_dataframe = hasattr(

C:\Users\Public\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _check_X(X)
    818     if hasattr(X, "__array__") or sparse.issparse(X):
    819         return X
--> 820     return check_array(X, force_all_finite="allow-nan", dtype=object)
    821 
    822 

C:\Users\Public\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    767             # If input is 1D raise error
    768             if array.ndim == 1:
--> 769                 raise ValueError(
    770                     "Expected 2D array, got 1D array instead:\narray={}.\n"
    771                     "Reshape your data either using array.reshape(-1, 1) if "

ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.