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.
      """
      # Forward through backbone model
      self.input = data.to(device)
  
file_path: torch-points3d/torch_points3d/models/object_detection/votenet.py
imports: import logging
  import numpy
  import torch
  import os
  from torch_geometric.data import Data
  from torch_points3d.datasets.object_detection.box_data import BoxData
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.applications import models
  import torch_points3d.modules.VoteNet
  from torch_points3d.models.base_architectures import UnetBasedModel
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
label_desc: Use image node_id 2 for calling the VoteNetModel obj's underlying member method code with example usage: obj.set_input(data, device) without return types