How to use the delve.kerascallback.CustomTensorBoard function in delve

x_test = np.random.random((100, 20))
y_test = keras.utils.to_categorical(
    np.random.randint(10, size=(100, 1)), num_classes=10)

# Build model
model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))

# Delve-specific
tbCallBack = CustomTensorBoard(log_dir='./runs', user_defined_freq=1)
saturation_logger = SaturationLogger(
    model, input_data=x_train[:2], print_freq=1)

# Train and evaluate model
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model = Model(model.get_input_at(0), outputs=model.output)
model.compile(
    loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

# # Optional - save to csv
# csv_logger = keras.callbacks.CSVLogger('1.log')
model.fit(
    x_train,
    y_train,
    epochs=100,

def on_epoch_begin(self, epoch, logs=None):
        """Add user-def. op to Model eval_function callbacks, reset batch count."""

        # check if histogram summary should be run for this epoch
        if self.user_defined_freq and epoch % self.user_defined_freq == 0:
            self._epoch = epoch
            # pylint: disable=protected-access
            # add the user-defined summary ops if it should run this epoch
            self.model._make_eval_function()
            if self.merged not in self.model._eval_function.fetches:
                self.model._eval_function.fetches.append(self.merged)
                self.model._eval_function.fetch_callbacks[
                    self.merged] = self._fetch_callback
            # pylint: enable=protected-access

        super(CustomTensorBoard, self).on_epoch_begin(epoch, logs=None)