Feeding multi-input .tfrecord-file to .fit()

Question

I try to train my model using a tfrecord dataset (800 GB). The simplified data pipeline looks like this:

files = tf.io.matching_files(tfr_dir + '*_' + single_pattern + '_*')
shards = tf.data.Dataset.from_tensor_slices(files)
# Read the tfrecords
dataset = tf.data.TFRecordDataset(filenames=shards, num_parallel_reads=tf.data.experimental.AUTOTUNE)
# Parse the tfrecords
dataset = dataset.map(parse_tfr_element, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# Apply image augmentation and parameter optimization using tf.py_function with defined Tout
dataset = self.dataset.map(imgaug)
# Sort out erroneous 
dataset = dataset.filter(lambda f1, f2, f3, f4, f5, state: state == False)
# Batch and prefetch data (not using shuffle atm)
dataset = dataset.batch(self.config.batch_size, num_parallel_calls=self.AUTOTUNE, drop_remainder=True)
dataset = dataset.prefetch(tf.data.AUTOTUNE) 

This gives me as output following (batch_size=8):

<RepeatDataset element_spec=
(TensorSpec(shape=(8, 4, 256, 256, 3), dtype=tf.float32, name=None), 
TensorSpec(shape=(8, 4, 19, 2), dtype=tf.float32, name=None), 
TensorSpec(shape=(8, 4, 19, 3), dtype=tf.float32, name=None), 
TensorSpec(shape=(8, 4, 3, 4), dtype=tf.float32, name=None),
TensorSpec(shape=(8, 4, 4, 3), dtype=tf.float32, name=None), 
TensorSpec(shape=(8,), dtype=tf.bool, name=None))>

dataset[0], dataset[3] and dataset[4] are the inputs (x) and dataset[1] and dataset[2] is the ground truth (y) (depending on the model).

This works well using a custom training loop iterating over the batches of the dataset using for step, data in enumerate(dataset) and defining the inputs to the model by simple subscribing e.g. data[0]. However, I can't get it running using .fit(). I tried different approaches to force .fit() to iterate over the dataset (next(iter(dataset), .from_generator()) but had no luck so far.

So how could I get a multi-input dataset into the fit function? I consider atm to not use tfrecords, as they were so far just hard to use.

Thanks for your help and all the best