code: def __init__(
      self, hidden_channels, num_filters, num_gaussians, cutoff
  ):
      super(update_e, self).__init__()
      self.cutoff = cutoff
      self.lin = Linear(hidden_channels, num_filters, bias=False)
      self.mlp = Sequential(
          Linear(num_gaussians, num_filters),
          ShiftedSoftplus(),
          Linear(num_filters, num_filters),
      )
  
      self.reset_parameters()
  
imports: from math import pi
  import torch
  import torch.nn.functional
  from torch.nn import Embedding, Sequential, Linear
  from torch_scatter import scatter
  from torch_geometric.nn import radius_graph
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, num_filters, num_gaussians, cutoff)