Does model.fit() reset metrics after each epoch? How to reset metrics manually?

As far as I understand, model.fit(epochs=NUM_EPOCHS) does not reset metrics for each epoch. My code for metrics and model.fit() looks like this (simplified):

import tensorflow as tf
from tensorflow.keras import applications

NUM_CLASSES = 4
INPUT_SHAPE = (256, 256, 3)
MODELS = {
    'DenseNet121': applications.DenseNet121,
    'DenseNet169': applications.DenseNet169
}
REDUCE_LR_PATIENCE = 2
REDUCE_LR_FACTOR = 0.7
EARLY_STOPPING_PATIENCE = 4


for modelName, model in MODELS.items():

    loadedModel = model(include_top=False, weights='imagenet',
                        pooling='avg', input_shape=INPUT_SHAPE)

    sequentialModel = tf.keras.models.Sequential()
    sequentialModel.add(loadedModel)
    sequentialModel.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax'))

    aucCurve = tf.keras.metrics.AUC(curve = 'ROC', multi_label = True)
    categoricalAccuracy = tf.keras.metrics.CategoricalAccuracy()
    F1Score  = tfa.metrics.F1Score(num_classes = NUM_CLASSES, average = 'macro', threshold = None)
    metrics = [aucCurve, categoricalAccuracy, F1Score]

    sequentialModel.compile(metrics=metrics)

    callbacks = [
    tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=REDUCE_LR_PATIENCE, verbose=1, factor=REDUCE_LR_FACTOR),
    tf.keras.callbacks.EarlyStopping(monitor='val_loss', verbose=1, patience=EARLY_STOPPING_PATIENCE),
    tf.keras.callbacks.ModelCheckpoint(filepath=modelName + '_epoch-{epoch:02d}.h5', monitor='val_loss', save_best_only=False, verbose=1),
    tf.keras.callbacks.CSVLogger(modelName + '_training.csv')]

    sequentialModel.fit(epochs=NUM_EPOCHS)

Perhaps I can reset metrics by doing a for loop in range of NUM_EPOCHS and initialize the metrics in a for loop, but I am not sure if it is a good solution. Also, I have ModelCheckpoint and CSVLogger callbacks, which require an epoch number from model.fit(), so it won't really work if I do a for loop.

Do you have any suggestions on how to reset metrics for each epoch? Is doing a for loop in range of NUM_EPOCHS the only solution here? Thank you.