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I tried to replicate the solution posted here with tf.data.Dataset.interleave, but not quite sure how to apply the interleave method to already created dataset objects. here is the code:

import tensorflow as tf
import numpy as np

# preparing data
train, test = tf.keras.datasets.fashion_mnist.load_data()
images, labels = train
images = images/255

dataset = tf.data.Dataset.from_tensor_slices((images, labels))

class0=lambda features, label: label==0
class1=lambda features, label: label==1
class2=lambda features, label: label==2

ds_0=dataset.filter(class0)
ds_1=dataset.filter(class1)
ds_2=dataset.filter(class2)

I want to create a dataset by equally sampled from the ds_0, ds_1, and ds_2. what should I pass as map_func?

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