code: def forward(self, z, pos, batch):
      # z, pos, batch = batch_data.z, batch_data.pos, batch_data.batch
  
      edge_index = radius_graph(pos, r=self.cutoff, batch=batch)
      num_nodes = z.size(0)
  
      dist, angle, torsion, i, j, idx_kj, idx_ji = xyztodat(
          pos, edge_index, num_nodes, batch
      )
  
      emb = self.emb(dist, angle, torsion, idx_kj)
  
      # Initialize edge, node, graph features
      init_e, node_type_emb = self.init_e(z, emb[0], i, j)
  
      e = init_e
      v = self.init_v(e, i, num_nodes)
      # u = self.init_u(torch.zeros_like(scatter(v, batch, dim=0)), v, batch) #scatter(v, batch, dim=0)
  
      for l in range(len(self.update_es) - 1):
          e = self.update_es[l](e, emb, idx_kj, idx_ji)
          v = self.update_vs[l](e, i, num_nodes)
          # u = self.update_us[l](u, v, batch) #u += scatter(v, batch, dim=0)
  
      e = self.update_es[-1](e, emb, idx_kj, idx_ji)
      v = self.update_vs[-1](e, i, num_nodes)
      v = node_type_emb + scatter(
          v[j] - node_type_emb[j],
          j,
          dim=0,
          reduce="mean",
          dim_size=num_nodes,
      )
      # u = self.update_us[-1](u, v, batch)
  
      return v
  
imports: import torch
  from torch import nn
  from torch.nn import Linear, Embedding
  from torch_geometric.nn.inits import glorot_orthogonal
  from torch_geometric.nn import radius_graph
  from torch_scatter import scatter
  import torch.nn.functional
  from math import sqrt
  from .geometric_computing import xyztodat
  from .features import dist_emb, angle_emb, torsion_emb
label_desc: Use image node_id 4 for calling the SphereNet obj's underlying member method code with example usage: obj.forward(z, pos, batch) and returns: v