code: def __init__(
      self,
      num_radial,
      hidden_channels,
      act=swish,
      use_node_features=True,
      use_extra_node_feature=False,
  ):
      super(init, self).__init__()
      self.act = act
      self.use_node_features = use_node_features
      self.use_extra_node_feature = use_extra_node_feature
      if self.use_node_features:
          self.emb = Embedding(95, hidden_channels)
      else:  # option to use no node features and a learned embedding vector for each node instead
          self.node_embedding = nn.Parameter(
              torch.empty((hidden_channels,))
          )
          nn.init.normal_(self.node_embedding)
      self.lin_rbf_0 = Linear(num_radial, hidden_channels)
      if self.use_extra_node_feature:
          self.lin = Linear(5 * hidden_channels, hidden_channels)
      else:
          self.lin = Linear(3 * hidden_channels, hidden_channels)
      self.lin_rbf_1 = nn.Linear(
          num_radial, hidden_channels, bias=False
      )
      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
  from math import sqrt
  from ...utils import xyz_to_dat
  from .features import dist_emb, angle_emb, torsion_emb
label_desc: Use image node_id 1 to create a new init object from inherited base classes: torch.nn with example: obj = init(num_radial, hidden_channels, act, use_node_features, use_extra_node_feature)