code: def __call__(self, sample):
      if sample.shape[1] >= self.output_size:
          return sample[0, : self.output_size]
      else:
          return torch.cat(
              (
                  sample[0, :],
                  torch.zeros(1, self.output_size - sample.shape[1]),
              ),
              axis=1,
          )
  
imports: import collections
  import importlib
  import logging
  import random
  import mmf.datasets.processors.functional
  import torch
  from mmf.common.registry import registry
  from mmf.datasets.processors import BaseProcessor
  from omegaconf import OmegaConf
  from torchvision import transforms
label_desc: Use image node_id 2 for calling the TruncateOrPad obj's underlying member method code with example usage: obj.__call__(sample) and returns: sample, torch