class_docstr: Universal Transformer(https://arxiv.org/pdf/1807.03819.pdf) with ACT(https://arxiv.org/pdf/1603.08983.pdf).
code: def forward(self, graph):
      g = graph.g
      N, E = graph.n_nodes, graph.n_edges
      nids, eids = graph.nids, graph.eids
  
      # embed & pos
      g.nodes[nids["enc"]].data["x"] = self.src_embed(graph.src[0])
      g.nodes[nids["dec"]].data["x"] = self.tgt_embed(graph.tgt[0])
      g.nodes[nids["enc"]].data["pos"] = graph.src[1]
      g.nodes[nids["dec"]].data["pos"] = graph.tgt[1]
  
      # init step
      device = next(self.parameters()).device
      g.ndata["s"] = th.zeros(
          N, self.h * self.d_k, dtype=th.float, device=device
      )  # accumulated state
      g.ndata["p"] = th.zeros(
          N, 1, dtype=th.float, device=device
      )  # halting prob
      g.ndata["r"] = th.ones(
          N, 1, dtype=th.float, device=device
      )  # remainder
      g.ndata["sum_p"] = th.zeros(
          N, 1, dtype=th.float, device=device
      )  # sum of pondering values
      g.ndata["step"] = th.zeros(
          N, 1, dtype=th.long, device=device
      )  # step
      g.ndata["active"] = th.ones(
          N, 1, dtype=th.uint8, device=device
      )  # active
  
      for step in range(self.MAX_DEPTH):
          pre_func = self.encoder.pre_func("qkv")
          post_func = self.encoder.post_func()
          nodes = g.filter_nodes(
              lambda v: v.data["active"].view(-1), nids["enc"]
          )
          if len(nodes) == 0:
              break
          edges = g.filter_edges(
              lambda e: e.dst["active"].view(-1), eids["ee"]
          )
          end = step == self.MAX_DEPTH - 1
          self.update_graph(
              g,
              edges,
              [(self.step_forward, nodes), (pre_func, nodes)],
              [
                  (post_func, nodes),
                  (self.halt_and_accum("enc", end), nodes),
              ],
          )
  
      g.nodes[nids["enc"]].data["x"] = self.encoder.norm(
          g.nodes[nids["enc"]].data["s"]
      )
  
      for step in range(self.MAX_DEPTH):
          pre_func = self.decoder.pre_func("qkv")
          post_func = self.decoder.post_func()
          nodes = g.filter_nodes(
              lambda v: v.data["active"].view(-1), nids["dec"]
          )
          if len(nodes) == 0:
              break
          edges = g.filter_edges(
              lambda e: e.dst["active"].view(-1), eids["dd"]
          )
          self.update_graph(
              g,
              edges,
              [(self.step_forward, nodes), (pre_func, nodes)],
              [(post_func, nodes)],
          )
  
          pre_q = self.decoder.pre_func("q", 1)
          pre_kv = self.decoder.pre_func("kv", 1)
          post_func = self.decoder.post_func(1)
          nodes_e = nids["enc"]
          edges = g.filter_edges(
              lambda e: e.dst["active"].view(-1), eids["ed"]
          )
          end = step == self.MAX_DEPTH - 1
          self.update_graph(
              g,
              edges,
              [(pre_q, nodes), (pre_kv, nodes_e)],
              [
                  (post_func, nodes),
                  (self.halt_and_accum("dec", end), nodes),
              ],
          )
  
      g.nodes[nids["dec"]].data["x"] = self.decoder.norm(
          g.nodes[nids["dec"]].data["s"]
      )
      act_loss = th.mean(g.ndata["r"])  # ACT loss
  
      self.stat[0] += N
      for step in range(1, self.MAX_DEPTH + 1):
          self.stat[step] += th.sum(g.ndata["step"] >= step).item()
  
      return (
          self.generator(g.ndata["x"][nids["dec"]]),
          act_loss * self.act_loss_weight,
      )
  
imports: from .attention import *
  from .layers import *
  from .functions import *
  from .embedding import *
  import dgl.function
  import torch
  import torch.nn.init
label_desc: Use image node_id 7 for calling the UTransformer obj's underlying member method code with example usage: obj.forward(graph) and returns: self,  unknown