class_docstr: Projects each input feature of the graph linearly and sums them up
class_docstr_tok: ["Projects","each","input","feature","of","the","graph","linearly","and","sums","them","up"]
code: def forward(self, ndata):
      projections = []
      for feature, data in ndata.items():
          if feature == dgl.NID or feature.endswith("__len"):
              # This is an additional feature indicating the length of the ``feature``
              # column; we shouldn't process this.
              continue
  
          module = self.inputs[feature]
          if isinstance(module, (BagOfWords, BagOfWordsPretrained)):
              # Textual feature; find the length and pass it to the textual module.
              length = ndata[feature + "__len"]
              result = module(data, length)
          else:
              result = module(data)
          projections.append(result)
  
      return torch.stack(projections, 1).sum(1)
  
imports: import argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
label_desc: Use image node_id 2 for calling the LinearProjector obj's underlying member method code with example usage: obj.forward(ndata) and returns: torch