tensorflow program terminates without running to the end

Question

I wrote the following code for image classification following the tutorial in the tensorflow website about dataset creation. The problem is that after creating the dataset variable, the code just stop executing and terminates.

path_ds = tf.data.Dataset.from_sparse_tensor_slices(all_images)
# PROGRAM TERMINATES AND DOES NOT EXECUTE THE FOLLOWING LINE 
image_ds = path_ds.map(load_image)

My initial guess was that it might be the problem with eager execution. I tried debugging using visual studio code debugger but could not get any far.

import os
import numpy as np 
import tensorflow as tf 
from PIL import Image

tf.compat.v1.enable_eager_execution()

test_folder = "../Test"
train_folder = "../Train"
BATCH_SIZE = 32

folders = os.listdir(train_folder)
labels = sorted(folders, key = lambda x : int(x.split("_")[1]) if "c" in x else \
                                  int(x.split("_")[1])+ 100)
char_to_int = dict((label, index) for index, label in enumerate(labels))
int_to_char = dict((index, label) for index, label in enumerate(labels))

def load_image(infilename) :
    img = Image.open( infilename )
    img.load()
    data = np.asarray( img, dtype="int32" )
    return tf.convert_to_tensor(np.expand_dims(data, axis=2))

def get_all_image_names(folder):
    children = os.listdir(folder)
    all_images = []
    all_labels = []
    for i in children:
        images = os.listdir(os.path.join(folder,i))
        for image in images:
            path = os.path.join(folder, i, image)
            all_images.append(path)
            all_labels.append(char_to_int[i])
    path_ds = tf.data.Dataset.from_sparse_tensor_slices(all_images)


    # PROGRAM TERMINATES AFTER THIS LINE 

    image_ds = path_ds.map(load_image)
    label_ds = tf.data.Dataset.from_sparse_tensor_slices(tf.cast(all_labels, tf.int64))
    image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))
    return image_label_ds

dataset = get_all_image_names(train_folder)

ds = dataset.shuffle(buffer_size=10000)
ds = ds.batch(BATCH_SIZE)

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(32, 32,1)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(64, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(46, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.summary()

model.fit(ds, epochs=55, verbose=1)

print("MOdel fitted")