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 forward(self, x, x_sub, pos, pos_sub, batch=None, batch_sub=None):
      # transform low-res features and reduce the number of features
      x_sub = self.mlp_sub(x_sub)
  
      # interpolate low-res feats to high-res points
      x_interpolated = knn_interpolate(
          x_sub, pos_sub, pos, k=3, batch_x=batch_sub, batch_y=batch
      )
  
      x = self.mlp(x) + x_interpolated
  
      return x
  
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 2 for calling the TransitionUp obj's underlying member method code with example usage: obj.forward(x, x_sub, pos, pos_sub, batch, batch_sub) and returns: x