ValueError: Found array with dim 4. Estimator expected <= 2. What should i do?

I would classify images with HBA-SVM but i have errors like this. My code on below.

x_train = []
y_train = []

x_test = []
y_test = []

for feature, label in train:
    x_train.append(feature)
    y_train.append(label)

for feature, label in test:
    x_test.append(feature)
    y_test.append(label)

Then normalize the data

x_train = np.array(x_train) / 255
x_test = np.array(x_test) / 255

Then, resize the data

# resize data
x_train = x_train.reshape(-1, img_size, img_size, 1) #img_size = 150
y_train = np.array(y_train)

x_test = x_test.reshape(-1, img_size, img_size, 1)
y_test = np.array(y_test)

then, aument the data using ImageDataGenerator

datagen = ImageDataGenerator(
    
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range = 0.2, # Randomly zoom image 
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip = True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

datagen.fit(x_train)

Then, set hyperparameter of SVM

param_grid = {
    'C': [0.1, 1, 10, 100, 1000],
    'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
    'kernel': ['rbf', 'poly', 'linear', 'sigmoid']
    } 

clf = SVC(random_state=42)

algorithm = HybridBatAlgorithm()
algorithm.set_parameters(NP=50, Ts=5, Mr=0.25)

After that, i would search the best hyperparameter

nia_search = NatureInspiredSearchCV(
    clf,
    param_grid,
    algorithm=algorithm,
    population_size=50,
    max_n_gen=100,
    max_stagnating_gen=20,
    runs=3,
)

nia_search.fit(x_train, y_train)    

But, my code resulted the error (on pict) like this ..

FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py", line 171, in fit
    accept_large_sparse=False)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/base.py", line 433, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 878, in check_X_y
    estimator=estimator)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 717, in check_array
    % (array.ndim, estimator_name))
ValueError: Found array with dim 4. Estimator expected <= 2.