code: def __init__(
      self,
      hidden_channels,
      int_emb_size,
      basis_emb_size,
      num_spherical,
      num_radial,
      num_before_skip,
      num_after_skip,
      act=swish,
  ):
      super(update_e, self).__init__()
      self.act = act
      self.lin_rbf1 = nn.Linear(num_radial, basis_emb_size, bias=False)
      self.lin_rbf2 = nn.Linear(
          basis_emb_size, hidden_channels, bias=False
      )
      self.lin_sbf1 = nn.Linear(
          num_spherical * num_radial, basis_emb_size, bias=False
      )
      self.lin_sbf2 = nn.Linear(
          basis_emb_size, int_emb_size, bias=False
      )
      self.lin_t1 = nn.Linear(
          num_spherical * num_spherical * num_radial,
          basis_emb_size,
          bias=False,
      )
      self.lin_t2 = nn.Linear(basis_emb_size, int_emb_size, bias=False)
      self.lin_rbf = nn.Linear(num_radial, hidden_channels, bias=False)
  
      self.lin_kj = nn.Linear(hidden_channels, hidden_channels)
      self.lin_ji = nn.Linear(hidden_channels, hidden_channels)
  
      self.lin_down = nn.Linear(
          hidden_channels, int_emb_size, bias=False
      )
      self.lin_up = nn.Linear(int_emb_size, hidden_channels, bias=False)
  
      self.layers_before_skip = torch.nn.ModuleList(
          [
              ResidualLayer(hidden_channels, act)
              for _ in range(num_before_skip)
          ]
      )
      self.lin = nn.Linear(hidden_channels, hidden_channels)
      self.layers_after_skip = torch.nn.ModuleList(
          [
              ResidualLayer(hidden_channels, act)
              for _ in range(num_after_skip)
          ]
      )
  
      self.reset_parameters()
  
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 1 to create a new update_e object from inherited base classes: torch.nn with example: obj = update_e(hidden_channels, int_emb_size, basis_emb_size, num_spherical, num_radial, num_before_skip, num_after_skip, act)