KeyError: 'Label not in explanation' for LIME Image Explainer for Neural Network for binary classification

Question

I am using the below code neural network & then trying to interpret the network using LIME

 import lime
 from lime import lime_image
 from skimage.segmentation import mark_boundaries

 model = Sequential()
 model.add(Conv2D(32, kernel_size=3, padding='same', activation='relu'))
 model.add(MaxPooling2D(pool_size=2))
 model.add(Flatten())
 # Output layer
 model.add(Dense(32,activation='relu'))
 model.add(Dense(1, activation='sigmoid'))

 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=epochs, batch_size=batch_size, verbose=1)


explainer = lime_image.LimeImageExplainer(random_state=42)
explanation = explainer.explain_instance(
             X_val[x] #some random sample, 
            model.predict, top_labels=2,labels=iter([0,1])
    )
image, mask = explanation.get_image_and_mask(1)

The last line is throwing error: KeyError: 'Label not in explanation' though working fine when passing 0 as parameter. But I wish to visualize for class 1.