Standalone code to reproduce the issue

Conv3DTranspose_class = tf.keras.layers.Conv3DTranspose(filters, kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint) layer = Conv3DTranspose_class inputs = __input___0 with tf.GradientTape() as g:

import tensorflow as tf import tensorflow_hub as hub mobilenet_v2 = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4" inception_v3 = "https://tfhub.dev/google/imagenet/inception_v3/classification/5" classifier_model = mobilenet_v2 # @param ["mobilenet_v2", "inception_v3"] {type:"raw"} IMAGE_SHAPE = (224, 224) classifier = tf.keras.Sequential([ hub.KerasLayer(classifier_model, input_shape=IMAGE_SHAPE + (3,)) ]) link to notebook: "https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning_with_hub.ipynb"