TypeError: Singleton array cannot be considered a valid collection - tensorflow

Question

Tried using k-cross validation from this link but with my own dataset and I got this error:
TypeError: Singleton array array(<BatchDataset element_spec=(TensorSpec(shape=(None, 180, 180, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>,
dtype=object) cannot be considered a valid collection.
Here is my code:
import numpy as np
import PIL
import tensorflow as tf
import os
from sklearn.model_selection import KFold
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
num_folds = 10
acc_per_fold = []
loss_per_fold = []
tf.get_logger().setLevel('ERROR')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
dataset_path = "data"
fullPath = os.path.abspath("./" + dataset_path)
#data_dir = tf.keras.utils.get_file('photos', origin='file://'+dataset_path, extract=True)
data_dir = pathlib.Path(fullPath)
image_count = len(list(data_dir.glob('*/*.jpg')))+len(list(data_dir.glob('*/*.png')))
print(image_count)
#man = list(data_dir.glob('man/*'))
#im = PIL.Image.open(str(man[28]))
#im.show()
batch_size = 32
img_height = 180
img_width = 180
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
labels='inferred',
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
labels='inferred',
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)
normalization_layer = layers.Rescaling(1./255)
# Define the K-fold Cross Validator
kfold = KFold(n_splits=num_folds, shuffle=True, random_state=42)
fold_no = 1
for train, test in kfold.split(train_ds, val_ds):
# Define the model architecture
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(no_classes, activation='softmax'))
# Compile the model
model.compile(loss=loss_function,
optimizer=optimizer,
metrics=['accuracy'])
# Generate a print
print('------------------------------------------------------------------------')
print(f'Training for fold {fold_no} ...')
# Fit data to model
history = model.fit(inputs[train], targets[train],
batch_size=batch_size,
epochs=no_epochs,
verbose=verbosity)
# Generate generalization metrics
scores = model.evaluate(inputs[test], targets[test], verbose=0)
print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')
acc_per_fold.append(scores[1] * 100)
loss_per_fold.append(scores[0])
# Increase fold number
fold_no = fold_no + 1
# == Provide average scores ==
print('------------------------------------------------------------------------')
print('Score per fold')
for i in range(0, len(acc_per_fold)):
print('------------------------------------------------------------------------')
print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')
print('------------------------------------------------------------------------')
print('Average scores for all folds:')
print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')
print(f'> Loss: {np.mean(loss_per_fold)}')
print('------------------------------------------------------------------------')