class_docstr: Reduce features dimensionality and interpolate back to higher
  resolution and cardinality.
class_docstr_tok: ["Reduce","features","dimensionality","and","interpolate","back","to","higher","resolution","and","cardinality","."]
code: def __init__(self, in_channels, out_channels):
      super().__init__()
      self.mlp_sub = MLP([in_channels, out_channels], plain_last=False)
      self.mlp = MLP([out_channels, out_channels], plain_last=False)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from point_transformer_classification import TransformerBlock, TransitionDown
  from torchmetrics.functional import jaccard_index
  import torch_geometric.transforms
  from torch_geometric.datasets import ShapeNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, knn_graph, knn_interpolate
  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 TransitionUp object from inherited base classes: torch.nn with example: obj = TransitionUp(in_channels, out_channels)