code: def _garnet(
      self,
      data,
      num_vertex,
      vertex_mask,
      in_transform,
      d_compute,
      out_transform,
      act_transform,
  ):
      features = in_transform(data)  # (B, V, F)
      distance = d_compute(data)  # (B, V, S)
  
      edge_weights = vertex_mask * K.exp(
          -K.square(distance)
      )  # (B, V, S)
  
      if not self._simplified:
          features = K.concatenate(
              [vertex_mask * features, edge_weights], axis=-1
          )
  
      if self._mean_by_nvert:
  
          def graph_mean(out, axis):
              s = K.sum(out, axis=axis)
              # reshape just to enable broadcasting
              s = (
                  K.reshape(
                      s, (-1, d_compute.units * in_transform.units)
                  )
                  / num_vertex
              )
              s = K.reshape(
                  s, (-1, d_compute.units, in_transform.units)
              )
              return s
  
      else:
          graph_mean = K.mean
  
      # vertices -> aggregators
      edge_weights_trans = K.permute_dimensions(
          edge_weights, (0, 2, 1)
      )  # (B, S, V)
  
      aggregated_mean = self._apply_edge_weights(
          features, edge_weights_trans, aggregation=graph_mean
      )  # (B, S, F)
  
      if self._simplified:
          aggregated = aggregated_mean
      else:
          aggregated_max = self._apply_edge_weights(
              features, edge_weights_trans, aggregation=K.max
          )
          aggregated = K.concatenate(
              [aggregated_max, aggregated_mean], axis=-1
          )
  
      # aggregators -> vertices
      updated_features = self._apply_edge_weights(
          aggregated, edge_weights
      )  # (B, V, S*F)
  
      if not self._simplified:
          updated_features = K.concatenate(
              [data, updated_features, edge_weights], axis=-1
          )
  
      return vertex_mask * act_transform(
          out_transform(updated_features)
      )
  
imports: import tensorflow.keras
  from qkeras import QActivation, QDense, ternary
label_desc: Use image node_id 8 for calling the GarNet obj's underlying member method code with example usage: obj._garnet(data, num_vertex, vertex_mask, in_transform, d_compute, out_transform, act_transform) and returns: unknown, s