Can you share more of your code and the TensorFlow version/environment you’re using?
Also, try using the latest TensorFlow version (e.g. with PiPI 4 or conda) and then call/use tf.keras.applications.vgg19().
Example/walkthrough with VGG19:

  
      

      TensorFlow
  

  
    

Neural style transfer  |  TensorFlow Core



  

  
    
    
  

  


API docs:

  
      

      TensorFlow
  

  
    

tf.keras.applications.VGG19  |  TensorFlow Core v2.5.0 3

  Instantiates the VGG19 architecture.


  

  
    
    
  

  


More examples with VGG19:

  
      

      TensorFlow
  

  
    

The Functional API  |  TensorFlow Core



  

  
    
    
  

  



  
      

      keras.io
  

  
    

Keras documentation: Keras Applications 2



  

  
    
    
  

  


This may also help - implementations of VGG-11/13/19 with TF 2.0 (last updated June 2020, not tested):

  

      github.com
  

  
    Bao-Jiarong/VGG/blob/master/vgg.py


    '''
  Author       : Bao Jiarong
  Creation Date: 2020-06-13
  email        : bao.salirong@gmail.com
  Task         : VGG11, VGG13, VGG16, VGG19 Implementation
  Dataset      : MNIST Digits (0,1,...,9)
'''
import tensorflow as tf

class Block(tf.keras.models.Sequential):
    def __init__(self,n,m):
        super().__init__()
        for i in range(m):
            self.add(tf.keras.layers.Conv2D(filters = n, kernel_size=(3,3),strides=(1,1),padding = 'same',activation = "relu"))
        self.add(tf.keras.layers.MaxPool2D(pool_size = (2, 2)))

class Dense(tf.keras.models.Sequential):
    def __init__(self,n,m=2):
        super().__init__()
        for i in range(m):



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