GradientTape returns None - tensorflow

Question

I am trying to use grad-CAM (I'm following this https://www.pyimagesearch.com/2020/03/09/grad-cam-visualize-class-activation-maps-with-keras-tensorflow-and-deep-learning/ from PyImageSearch) on a CNN I'm using transfer learning on.
In particular, I am using a simple CNN for a regression problem. I used MobileNetV2 with an Average Pooling layer and a Dense layer with one unit on top, as shown below:
base_model = MobileNetV2(include_top=False, input_shape=(224, 224, 3), weights='imagenet')
base_model.trainable = False
inputs = keras.Input(shape=(224, 224, 3))
x = base_model(inputs)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(1, activation="linear")(x)
model = keras.Model(inputs, outputs)
and the summary is:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280) 2257984
_________________________________________________________________
global_average_pooling2d (Gl (None, 1280) 0
_________________________________________________________________
dense (Dense) (None, 1) 1281
=================================================================
Total params: 2,259,265
Trainable params: 1,281
Non-trainable params: 2,257,984
_________________________________________________________________
I initialize the CAM object with:
pred = 0.35
cam = GradCAM(model, pred, layerName='input_2')
where pred is the predicted output on which I want to inspect the CAM and I also specify the layer name in order to refer to the input layer. Then I compute the heatmap on a sample image "img":
heatmap = cam.compute_heatmap(img)
Now, let's focus on a part of the implementation of the function compute_heatmap from PyImageSearch:
# record operations for automatic differentiation
with tf.GradientTape() as tape:
# cast the image tensor to a float-32 data type, pass the
# image through the gradient model, and grab the loss
# associated with the specific class index
inputs = tf.cast(image, tf.float32)
(convOutputs, predictions) = gradModel(inputs)
# loss = predictions[:, self.classIdx] # original from PyImageSearch
loss = predictions[:] # modified by me as I have only 1 output unit
# use automatic differentiation to compute the gradients
grads = tape.gradient(loss, convOutputs)
The problem here is that the gradient grads is None.
I thought that maybe the problem could lie in the network structure (all goes fine when reproducing the example of the classification task from the website), but I can't figure out where is the problem with this network used for regression!
Could you please help me?