tf2_stackoverflow_question

# version 2.1.0
import tensorflow as tf

# version 1.18.1
import numpy as np


# ######## DEFINE CUSTOM FUNCTION FOR TF LAMBDA LAYER  ######## #
def resize_like(input_tensor, ref_tensor):
    """ Resize an image tensor to the same size/shape as a reference image tensor

    Args:
        input_tensor : (image tensor) Input image tensor that will be resized
        ref_tensor   : (image tensor) Reference image tensor that we want to resize the input tensor to.

    Returns:
        reshaped tensor
    """
    reshaped_tensor = tf.image.resize(images=input_tensor,
                                      size=tf.shape(ref_tensor)[1:3],
                                      method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
                                      preserve_aspect_ratio=False,
                                      antialias=False,
                                      name=None)
    return reshaped_tensor
# ############################################################# #

# ############ DEFINE MODEL USING TF.KERAS FN API  ############ #

# INPUTS
model_input_1 = tf.keras.layers.Input(shape=(160,160,3))
model_input_2 = tf.keras.layers.Input(shape=(160,160,3))

# OUTPUTS
model_output_1 = tf.keras.layers.Conv2D(filters=64, 
                                        kernel_size=(1, 1), 
                                        use_bias=False,
                                        kernel_initializer='he_normal',
                                        name='conv_name_base')(model_input_1)
model_output_2 = tf.keras.layers.Lambda(function=resize_like,
                                        arguments={'ref_tensor': model_output_1})(model_input_2)

# MODEL
model = tf.keras.models.Model(inputs=[model_input_1, model_input_2],
                                      outputs=model_output_2,
                                      name="test_model")
                                      
# ############################################################# #

# ######### TRY TO UTILIZE PREDICT WITH DUMMY INPUT  ########## #

dummy_input = [np.ones((1,160,160,3)), np.zeros((1,160,160,3))]
model.predict(x=dummy_input) # >>>>ERROR OCCURS HERE<<<<

# ############################################################# #