ML model giving rank errors on 3D layers on converting 2D images to 3D models

Question

i am currently working on a model to convert 2d images to 3d models through a ml model. For this i have taken into reference a research paper which had this diagrammatical flow of layers & i have tried to put that into work but in vain. Would really like to know where i am going wrong, looking forward for help and suggestions. Thanks! reference image for model-

my code-

image = Input(shape=(None, None, 3))

# Encoder
l1 = Conv2D(64, (3,3), strides = (2), padding='same', activation='leaky_relu')(image)  
l2 = MaxPooling2D()(l1)
l3 = Conv2D(32, (5,5), strides = (2), padding='same', activation='leaky_relu')(l2)
l4 = MaxPooling2D(padding='same')(l3)
l5 = Conv2D(16, (7,7), strides = (2), padding='same', activation='leaky_relu')(l4)
l6 = MaxPooling2D(padding='same')(l5)
l7 = Conv2D(8, (5, 5), strides = (2), padding = 'same', activation = 'leaky_relu')(l6)
l8 = MaxPooling2D(padding='same')(l7)
l9 = Conv2D(4, (3, 3), strides = (2), padding = 'same', activation = 'leaky_relu')(l8)
l10 = MaxPooling2D(padding='same')(l9)
l11 = Conv2D(2, (4, 4), strides = (2), padding = 'same', activation = 'leaky_relu')(l10)
l12 = MaxPooling2D(padding='same')(l11)
l13 = Conv2D(1, (2, 2), strides = (2), padding = 'same', activation = 'leaky_relu')(l12)



# latent variable z
l14 = Reshape((60,512))(l13)
l15 = Dense((512),activation = 'leaky_relu')(l14)
l16 = Dense((128*4*4*4), activation = 'leaky_relu')(l15)
l17 = Reshape((60,4,4,4,128))(l16)

#Decoder
l18 = UpSampling3D(size = (3,3,3))(l17) #-->throws error->IndexError: list index out of range
l19 = Conv3DTranspose(60, (8, 8, 8), strides = (64), padding='same', activation = 'leaky_relu') (l17)
l20 = UpSampling3D((3,3,3))(l19)
l21 = Conv3DTranspose(60, (16,16,16), strides =(32), padding='same', activation = 'leaky_relu')(l20)
l22 = UpSampling3D((3,3,3))(l21)
l23 = Conv3DTranspose(60, (32, 32, 32), strides = (32), padding='same', activation = 'lealy_relu')(l22)
l24 = UpSampling3D((3,3,3))(l23)
l25 = Conv3DTranspose(60, (64, 64, 64), strides = (24), padding='same', activation = 'leaky_relu')(l24)
l26 = UpSampling3D((3,3,3))(l25)
l27 = Conv3DTranspose(60, (64, 64, 64), strides = (1), padding='same', activation = 'leaky_relu')(l26)

model3D = Model(image, l27)

error-

ValueError                                Traceback (most recent call last)
/tmp/ipykernel_17/3800219535.py in <module>
     34 #Decoder
     35 l18 = UpSampling3D(size = (3,3,3))(l17) #-->throws error->IndexError: list index out of range
---> 36 l19 = Conv3DTranspose(60, (8, 8, 8), strides = (64), padding='same', activation = 'leaky_relu') (l17)
     37 l20 = UpSampling3D((3,3,3))(l19)
     38 l21 = Conv3DTranspose(60, (16,16,16), strides =(32), padding='same', activation = 'leaky_relu')(l20)

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    975     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    976       return self._functional_construction_call(inputs, args, kwargs,
--> 977                                                 input_list)
    978 
    979     # Maintains info about the `Layer.call` stack.

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1113       # Check input assumptions set after layer building, e.g. input shape.
   1114       outputs = self._keras_tensor_symbolic_call(
-> 1115           inputs, input_masks, args, kwargs)
   1116 
   1117       if outputs is None:

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    846       return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature)
    847     else:
--> 848       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    849 
    850   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    884           # overridden).
    885           # TODO(kaftan): do we maybe_build here, or have we already done it?
--> 886           self._maybe_build(inputs)
    887           inputs = self._maybe_cast_inputs(inputs)
    888           outputs = call_fn(inputs, *args, **kwargs)

/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in _maybe_build(self, inputs)
   2657         # operations.
   2658         with tf_utils.maybe_init_scope(self):
-> 2659           self.build(input_shapes)  # pylint:disable=not-callable
   2660       # We must set also ensure that the layer is marked as built, and the build
   2661       # shape is stored since user defined build functions may not be calling

/opt/conda/lib/python3.7/site-packages/keras/layers/convolutional.py in build(self, input_shape)
   1546     if len(input_shape) != 5:
   1547       raise ValueError('Inputs should have rank 5, received input shape:',
-> 1548                        str(input_shape))
   1549     channel_axis = self._get_channel_axis()
   1550     if input_shape.dims[channel_axis].value is None:

ValueError: ('Inputs should have rank 5, received input shape:', '(None, 60, 4, 4, 4, 128)')```