Grad-CAM in keras, ValueError: Graph disconnected: cannot obtain value for tensor Tensor "input_11_6:0", shape=(None, 150, 150, 3)

How to perform Grad-CAM on pretrained custom model. How to select last_conv_layer_name and classifier_layer_names? What is its significances and how to select layers' names? Should I consider Densenet121 sublayers or densenet as one functional layer? How to perform Grad-CAM for this trained network? These are the steps I tried,

#load model and custom metrics
dependencies = {'recall_m': recall_m, 'precision_m' : precision_m, 'f1_m' : f1_m }
model = keras.models.load_model("model_val_acc-73.33.h5", custom_objects = dependencies)
model.summary()

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
densenet121 (Functional)     (None, 4, 4, 1024)        7037504   
_________________________________________________________________
flatten (Flatten)            (None, 16384)             0         
_________________________________________________________________
dense_encoder (Dense)        (None, 1024)              16778240  
_________________________________________________________________
dropout_51 (Dropout)         (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 256)               262400    
_________________________________________________________________
dropout_52 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 128)               32896     
_________________________________________________________________
dropout_53 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 64)                8256      
_________________________________________________________________
dropout_54 (Dropout)         (None, 64)                0         
_________________________________________________________________
dense_5 (Dense)              (None, 32)                2080      
_________________________________________________________________
dropout_55 (Dropout)         (None, 32)                0         
_________________________________________________________________
Final (Dense)                (None, 2)                 66        
=================================================================
Total params: 24,121,442
Trainable params: 17,083,938
Non-trainable params: 7,037,504

This is the heat map function:-

###defining heat map

def make_gradcam_heatmap(img_array, model, last_conv_layer_name, classifier_layer_names):
    # First, we create a model that maps the input image to the activations
    # of the last conv layer
    last_conv_layer = model.get_layer(last_conv_layer_name)
    last_conv_layer_model = keras.Model(model.inputs, last_conv_layer.output)

    # Second, we create a model that maps the activations of the last conv
    # layer to the final class predictions
    classifier_input = keras.Input(shape=last_conv_layer.output.shape[1:])
    x = classifier_input
    for layer_name in classifier_layer_names:
        x = model.get_layer(layer_name)(x)
    classifier_model = keras.Model(classifier_input, x)

    # Then, we compute the gradient of the top predicted class for our input image
    # with respect to the activations of the last conv layer
    with tf.GradientTape() as tape:
        # Compute activations of the last conv layer and make the tape watch it
        last_conv_layer_output = last_conv_layer_model(img_array)
        tape.watch(last_conv_layer_output)
        # Compute class predictions
        preds = classifier_model(last_conv_layer_output)
        top_pred_index = tf.argmax(preds[0])
        top_class_channel = preds[:, top_pred_index]

    # This is the gradient of the top predicted class with regard to
    # the output feature map of the last conv layer
    grads = tape.gradient(top_class_channel, last_conv_layer_output)

    # This is a vector where each entry is the mean intensity of the gradient
    # over a specific feature map channel
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))

    # We multiply each channel in the feature map array
    # by "how important this channel is" with regard to the top predicted class
    last_conv_layer_output = last_conv_layer_output.numpy()[0]
    pooled_grads = pooled_grads.numpy()
    for i in range(pooled_grads.shape[-1]):
        last_conv_layer_output[:, :, i] *= pooled_grads[i]

    # The channel-wise mean of the resulting feature map
    # is our heatmap of class activation
    heatmap = np.mean(last_conv_layer_output, axis=-1)

    # For visualization purpose, we will also normalize the heatmap between 0 & 1
    heatmap = np.maximum(heatmap, 0) / np.max(heatmap)
    return heatmap

this is an image input

img_array = X_test[10]    # 10th image sample
X_test[10].shape
#(150, 150, 3)

last_conv_layer_name = "densenet121"
classifier_layer_names = [ "dense_2", "dense_3", "dense_4", "dense_5", "Final" ]

# Generate class activation heatmap
heatmap = make_gradcam_heatmap(
    img_array, model, last_conv_layer_name, classifier_layer_names
)  ####===> (I'm getting error here, in this line)

So what is wrong with last_conv_layer_name and classifier_layer_names. Can anyone please explain this?