code: def get_data_batch(
      batch_size, num_labels, num_channels=3, height=224, width=224
  ):
      pixel_values = torch.randn(
          batch_size,
          num_channels,
          height,
          width,
          device=torch.cuda.current_device(),
          dtype=torch.float,
      )
      labels = torch.randint(
          0,
          num_labels,
          (batch_size,),
          device=torch.cuda.current_device(),
          dtype=torch.int64,
      )
      return dict(pixel_values=pixel_values, labels=labels)
  
file_path: ColossalAI/examples/images/vit/vit_benchmark.py
imports: import time
  import torch
  import transformers
  from args import parse_benchmark_args
  from tqdm import tqdm
  from transformers import ViTConfig, ViTForImageClassification
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
  from colossalai.cluster import DistCoordinator
  from colossalai.logging import disable_existing_loggers, get_dist_logger
  from colossalai.nn.optimizer import HybridAdam
label_desc: Use image node_id 2 for calling a global function with example usage: get_data_batch(batch_size, num_labels, num_channels, height, width) and returns: dict