class_docstr: Upsampling with a skip connection.
code: def __init__(self, k, nn):
      super().__init__()
      self.k = k
      self.nn = nn
  
imports: import os.path
  import torch
  import torch.nn.functional
  from randlanet_classification import DilatedResidualBlock, SharedMLP, decimate
  from torch.nn import Linear
  from torchmetrics.functional import jaccard_index
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ShapeNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import 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 FPModule object from inherited base classes: torch.nn with example: obj = FPModule(k, nn)