class_docstr: Feature aggregation
class_docstr_tok: ["Feature","aggregation"]
code: def __init__(self, sizes, batch_size):
      super(KNNConv, self).__init__()
      self.batch_size = batch_size
      self.conv = nn.ModuleList()
      self.bn = nn.ModuleList()
      for i in range(1, len(sizes)):
          self.conv.append(nn.Conv2d(sizes[i - 1], sizes[i], 1))
          self.bn.append(nn.BatchNorm2d(sizes[i]))
  
imports: import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.geometry import farthest_point_sampler
label_desc: Use image node_id 1 to create a new KNNConv object from inherited base classes: nn with example: obj = KNNConv(sizes, batch_size)