code: def _unpack_input(self, x):
      if self._input_format == "x":
          data = x
  
          vertex_mask = K.cast(
              K.not_equal(data[..., 3:4], 0.0), "float32"
          )
          num_vertex = K.sum(vertex_mask)
  
      elif self._input_format in ["xn", "xen"]:
          if self._input_format == "xn":
              data, num_vertex = x
          else:
              data_x, data_e, num_vertex = x
              data = K.concatenate(
                  (data_x, K.reshape(data_e, (-1, data_e.shape[1], 1))),
                  axis=-1,
              )
  
          data_shape = K.shape(data)
          B = data_shape[0]
          V = data_shape[1]
          vertex_indices = K.tile(
              K.expand_dims(K.arange(0, V), axis=0), (B, 1)
          )  # (B, [0..V-1])
          vertex_mask = K.expand_dims(
              K.cast(
                  K.less(vertex_indices, K.cast(num_vertex, "int32")),
                  "float32",
              ),
              axis=-1,
          )  # (B, V, 1)
          num_vertex = K.cast(num_vertex, "float32")
  
      return data, num_vertex, vertex_mask
  
imports: import tensorflow.keras
  from qkeras import QActivation, QDense, ternary
label_desc: Use image node_id 7 for calling the GarNet obj's underlying member method code with example usage: obj._unpack_input(x) and returns: data,  num_vertex,  vertex_mask