How to correctly organize Tensorflow model layers?

Hellow world!
We are writing our own AI and we struggle to create correct model layer.
What we have to input in our neural network is a list that contains n lists with m tuples

e.x. list = numpy.array([ [[1,2,4],[5,6,8]] , [[5,6,0],[7,2,4]] ])

What we expect to get as a result is either 0 or 1 ( it makes sense trust me )

This is what we have for now:

tpl = 3 # because we have tuples
nl = 2 # number of lists we have
model = tf.keras.Sequential([
# this should be entry layer that understands our list
            tf.keras.layers.Dense(nl * tpl , input_shape=(nl, tpl), activation='relu'),

#hidden layers..
            tf.keras.layers.Dense(64, input_shape=(nl, tpl), activation='sigmoid'),

#our output layer with 2 nodes that one should contain 0, other 1, because we have 2 labels ( 0 and 1 )
            tf.keras.layers.Dense(2, input_shape=(0, 1), activation='softmax')
        ])

But we get the error below:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

InvalidArgumentError:  Incompatible shapes: [56,2,2] vs. [56,1]
     [[node huber_loss/Sub (defined at <ipython-input-25-08eb2e0b395e>:53) ]] [Op:__inference_train_function_45699]

Function call stack:
train_function

If we summarize our model, it gives, the following structure:

Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)             (None, 2, 6)              24        
_________________________________________________________________
dense_2 (Dense)             (None, 2, 64)             448       
_________________________________________________________________
dense_3 (Dense)             (None, 2, 2)              130       
=================================================================

Finally,

What we have understood is that our provided data is not compatible with the last layer, so how do we convert the last layer to => shape (None, 2) or what is the right way to work this error out?

import numpy
import tensorflow as tf

list = numpy.array([[[1., 2., 4.], [5., 6., 8.]], [[5., 6., 0.], [7., 2., 4.]]])

tpl = 3  
nl = 2   
model = tf.keras.Sequential([
    tf.keras.layers.Dense(nl * tpl, input_shape=(nl, tpl), activation='relu'),
    tf.keras.layers.Dense(64, input_shape=(nl, tpl), activation='sigmoid'),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(2, input_shape=(0, 1), activation='softmax')
])

model.build(input_shape=(nl, tpl))

model(list)

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[0.41599566, 0.58400434],
       [0.41397247, 0.58602756]], dtype=float32)>

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_12 (Dense)             (None, 2, 6)              24        
_________________________________________________________________
dense_13 (Dense)             (None, 2, 64)             448       
_________________________________________________________________
global_average_pooling1d (Gl (None, 64)                0         
_________________________________________________________________
dense_14 (Dense)             (None, 2)                 130       
=================================================================
Total params: 602
Trainable params: 602
Non-trainable params: 0
_________________________________________________________________