[Python] How to use loaded data from kaggle in google colab( to actually work with it)

So i recently imported dataset from this https://www.kaggle.com/datasets/mostafaabla/garbage-classification website. And even though i have it in my files in google colab(unzipped and all that stuff), i dont know how to implement it in the code itself. Like from tutorial of Fashion mnist by tensorflow https://www.tensorflow.org/tutorials/keras/classification?hl it loaded as

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

how do i import/load data to code cell and work with it by splitting into classes(because in that tutorial dataset has several classes and in my custom dataset it has 12) pls how to do it?

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define paths to your training and validation directories
train_dir = 'garbage-classification/train'
val_dir = 'garbage-classification/validation'

# Create an ImageDataGenerator for data augmentation
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

# Load images from directories
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150), # Resize images as needed
batch_size=32,
class_mode='categorical' # Use 'categorical' if you have multiple classes
)

validation_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical'
)


i used perplexity to try to solve and it gave me this. It obviously didn't work so..

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