code: def __init__(
      self,
      hidden_channels,
      num_radial,
      num_spherical,
      num_layers,
      output_channels,
      act=swish,
      hetero=False,
      inits="glorot",
  ):
      super(SimpleInteractionBlock, self).__init__()
      self.act = act
  
      self.conv1 = EdgeGraphConv(hidden_channels, hidden_channels)
  
      self.conv2 = EdgeGraphConv(hidden_channels, hidden_channels)
  
      self.lin1 = Linear(hidden_channels, hidden_channels)
  
      self.lin2 = Linear(hidden_channels, hidden_channels)
  
      self.lin_cat = Linear(2 * hidden_channels, hidden_channels)
  
      self.norm = GraphNorm(hidden_channels)
  
      # Transformations of Bessel and spherical basis representations.
      self.lin_feature1 = TwoLayerLinear(
          num_radial * num_spherical**2,
          hidden_channels,
          hidden_channels,
          hetero=hetero,
      )
      self.lin_feature2 = TwoLayerLinear(
          num_radial * num_spherical,
          hidden_channels,
          hidden_channels,
          hetero=hetero,
      )
  
      # Dense transformations of input messages.
      if hetero:
          self.lin = HeteroLinear(
              hidden_channels, hidden_channels, num_tags=3
          )
          self.lins = torch.nn.ModuleList()
          for _ in range(num_layers):
              self.lins.append(
                  HeteroLinear(
                      hidden_channels, hidden_channels, num_tags=3
                  )
              )
          self.final = HeteroLinear(
              hidden_channels,
              output_channels,
              num_tags=3,
              weight_initializer=inits,
          )
      else:
          self.lin = Linear(hidden_channels, hidden_channels)
          self.lins = torch.nn.ModuleList()
          for _ in range(num_layers):
              self.lins.append(Linear(hidden_channels, hidden_channels))
          self.final = Linear(
              hidden_channels, output_channels, weight_initializer=inits
          )
  
      self.reset_parameters()
  
imports: from torch_cluster import radius_graph
  from torch_geometric.nn import GraphConv, GraphNorm
  from torch_geometric.nn import inits
  from torch_scatter import scatter, scatter_min
  from torch.nn import Embedding
  import torch
  from torch import nn
  from torch import Tensor
  import torch.nn.functional
  import math
  from math import sqrt
label_desc: Use image node_id 1 to create a new SimpleInteractionBlock object from inherited base classes: torch.nn with example: obj = SimpleInteractionBlock(hidden_channels, num_radial, num_spherical, num_layers, output_channels, act, hetero, inits)