docstr: Unpack input data from the dataloader and perform necessary pre-processing steps.
  Parameters:
      input: a dictionary that contains the data itself and its metadata information.
docstr_tok: ["Unpack","input","data","from","the","dataloader","and","perform","necessary","pre-processing","steps",".","Parameters",":","input",":","a","dictionary","that","contains","the","data","itself","and","its","metadata","information","."]
code: def set_input(self, data, device):
      """Unpack input data from the dataloader and perform necessary pre-processing steps.
      Parameters:
          input: a dictionary that contains the data itself and its metadata information.
      """
      self.semantic_labels = data.y.flatten().to(self.device)
      if self.is_dense_format:
          self.input = data
          self._n_batches = data.pos.shape[0]
      else:
          if self._kpconv_backbone:
              data = AddOnes()(data)
              if data.x is not None:
                  data.x = torch.cat(
                      [data.x, data.ones.float()], dim=-1
                  )
              else:
                  data.x = data.ones.float()
          self.input = data
          self._n_batches = torch.max(data.batch) + 1
  
imports: import torch
  from torch_geometric.data import Data
  import os
  from torch_points3d.datasets.object_detection.box_data import BoxData
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.applications import models
  import torch_points3d.modules.VoteNet
  from torch_points3d.core.common_modules import Seq, MLP, FastBatchNorm1d
  from torch_points3d.core.data_transform import AddOnes
  from torch_points3d.modules.VoteNet.dense_samplers import RandomSamplerToDense, FPSSamplerToDense
label_desc: Use image node_id 2 for calling the VoteNet2 obj's underlying member method code with example usage: obj.set_input(data, device) without return types