Got ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor

When I tried to run a colab notebook on 2021 June, which was created on 2020 december and ran fine I got an error. So I changed

baseModel = tf.keras.applications.VGG16(weights="imagenet", 
                                     include_top= False,
                                     input_tensor=Input(shape=(224, 224, 3)))

to

baseModel = tf.keras.applications.VGG19(weights="imagenet", 
                                     include_top= False,
                                     input_shape=(224, 224, 3))

However when I continued to execute the notebook I got an error "ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor." in a later stage.

Code:

import numpy as np
from tqdm import tqdm
import math
import os

import keras
from keras.models import *
from keras.layers import *
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from sklearn.metrics import confusion_matrix
from keras.applications.densenet import DenseNet121
from keras.callbacks import *
from keras import backend as K
K.clear_session()
import itertools
import matplotlib.pyplot as plt
import cv2
import matplotlib.cm as cm

from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer,LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix


import tensorflow as tf
baseModel = tf.keras.applications.VGG19(weights="imagenet", 
                                     include_top= False,
                                     input_shape=(224, 224, 3))

headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(4, 4))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(64, activation="relu")(headModel)
headModel = Dropout(0.4)(headModel)
headModel = Dense(3, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)

model.summary()

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-18-6695ac43a942> in <module>()
      1 headModel = baseModel.output
      2 headModel = AveragePooling2D(pool_size=(4, 4))(headModel)
----> 3 headModel = Flatten(name="flatten")(headModel)
      4 headModel = Dense(64, activation="relu")(headModel)
      5 headModel = Dropout(0.4)(headModel)

5 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
     96       dtype = dtypes.as_dtype(dtype).as_datatype_enum
     97   ctx.ensure_initialized()
---> 98   return ops.EagerTensor(value, ctx.device_name, dtype)
     99 
    100 

ValueError: Attempt to convert a value (None) with an unsupported type (<class 'NoneType'>) to a Tensor.

Updated imports:

import numpy as np
from tqdm import tqdm
import math
import os

import tensorflow as tf

import tensorflow.keras
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from sklearn.metrics import confusion_matrix
from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.callbacks import *
from tensorflow.keras import backend as K
K.clear_session()
import itertools
import matplotlib.pyplot as plt
import cv2
import matplotlib.cm as cm

from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer,LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix