Sample Importance (Training Weights) in Keras
I'm not sure I completely understand what you want but it looks like you're trying to find an implementation for sample_weight. Well, I had tried something similar. Before I go into it, I want to mention that my error actually went up - I haven't had the time to go into why that happened. 

For simplicity, lets say you know the weights you need for each class and can pass it as a dictionary. In my example I have my y_train as a one hot encoded vector. I'm using that fact to reverse engineer which class each row is pointing to and adding a weight for it. You essentially need to pass an array of weights mapping to each label (so the same length as your training data) when you fit the model.

def generate_sample_weights(training_data, class_weight_dictionary): 
    sample_weights = [class_weight_dictionary[np.where(one_hot_row==1)[0][0]] for one_hot_row in training_data]
    return np.asarray(sample_weights)


And this is passed when you call fit on the model.

model.fit(x=X_train, 
    y=y_train, 
    batch_size = 64,
    validation_data=(X_val, y_val),
    shuffle=True,
    epochs=20,
    sample_weight = generate_sample_weights(y_train, class_weights_dict)
)