Could you help to combine input layers with a specific NamedTuple class?

Hello, I’ve been searching/reading for a fair amount of hours, but I’m pretty much stuck with this problem.

This is my code:

from typing import NamedTuple class MessagePassingInput(NamedTuple): node_embeddings: tf.Tensor adjacency_lists: Tuple[tf.Tensor, ...] from keras import Model, layers import tensorflow as tf inputLayer_X = layers.Input(shape=tf.TensorShape(dims=(None, 7)),name="Input_X") inputLayer_A1 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A1", dtype=tf.int32) inputLayer_A2 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A2", dtype=tf.int32) inputLayer_A3 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A3", dtype=tf.int32) 

And I would like that every entry in those inputs ends up in a next layer more or less like this: newLayer = [MessagePassingInput(inputLayer_X[i], [inputLayer_A1[i], inputLayer_A2[i], inputLayer_A3[i]]) for i in range(len(inputLayer_X))]. However, I’m just not being able to find how (I have tried with tf.map_fn and layers.Lambda, but wasn’t able to feed all those input layers and use the function in order)

If you could help me, I would be very grateful šŸ™

submitted by /u/jorvan758
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