class_docstr: A single PNA tower in PNA layers
class_docstr_tok: ["A","single","PNA","tower","in","PNA","layers"]
code: def __init__(
      self,
      in_size,
      out_size,
      aggregators,
      scalers,
      delta,
      dropout=0.0,
      edge_feat_size=0,
  ):
      super(PNAConvTower, self).__init__()
      self.in_size = in_size
      self.out_size = out_size
      self.aggregators = aggregators
      self.scalers = scalers
      self.delta = delta
      self.edge_feat_size = edge_feat_size
  
      self.M = nn.Linear(2 * in_size + edge_feat_size, in_size)
      self.U = nn.Linear(
          (len(aggregators) * len(scalers) + 1) * in_size, out_size
      )
      self.dropout = nn.Dropout(dropout)
      self.batchnorm = nn.BatchNorm1d(out_size)
  
imports: import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new PNAConvTower object from inherited base classes: nn with example: obj = PNAConvTower(in_size, out_size, aggregators, scalers, delta, dropout, edge_feat_size)