class_docstr: Samples the input point cloud by a ratio percentage to reduce
  cardinality and uses an mlp to augment features dimensionnality.
class_docstr_tok: ["Samples","the","input","point","cloud","by","a","ratio","percentage","to","reduce","cardinality","and","uses","an","mlp","to","augment","features","dimensionnality","."]
code: def __init__(self, in_channels, out_channels, ratio=0.25, k=16):
      super().__init__()
      self.k = k
      self.ratio = ratio
      self.mlp = MLP([in_channels, out_channels], plain_last=False)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, PointTransformerConv, fps, global_mean_pool, knn, knn_graph
  from torch_geometric.typing import WITH_TORCH_CLUSTER
  from torch_geometric.utils import scatter
label_desc: Use image node_id 1 to create a new TransitionDown object from inherited base classes: torch.nn with example: obj = TransitionDown(in_channels, out_channels, ratio, k)