Tensorflow training with batch size of (1, None, features), but model expects extra dimension
I managed to solve my problem with a generator, which expands the dimensions for each single batch to return shape (1, None, 2).

class SingleBatchGenerator:
    def __init__(self, X):
        self.X = X

    def __call__(self):
        for i in range(len(self.X)):
            xi = np.expand_dims(self.X[i], axis=0)
            yield xi, xi

X = [np.ones((np.random.randint(1, 100), 2)) for _ in range(100)]
gen = SingleBatchGenerator(X)

ds = tf.data.Dataset.from_generator(
    generator = gen,
    output_types=(tf.float64, tf.float64),
    output_shapes=((1, None, 2), (1, None, 2)),
)

autoenc.fit(ds.repeat(), steps_per_epoch=len(X), epochs=500)