code: def ids_tensor(shape, vocab_size, rng=None, name=None):
      #  Creates a random int32 tensor of the shape within the vocab size
      if rng is None:
          rng = global_rng
  
      total_dims = 1
      for dim in shape:
          total_dims *= dim
  
      values = []
      for _ in range(total_dims):
          values.append(rng.randint(0, vocab_size - 1))
  
      return (
          torch.tensor(
              data=values, dtype=torch.long, device=torch_device
          )
          .view(shape)
          .contiguous()
      )
  
imports: import random
  import datasets
  import torch
  import adapters
  from adapters import AutoAdapterModel
  from transformers import AutoFeatureExtractor, AutoTokenizer, GlueDataset, GlueDataTrainingArguments
  from transformers.testing_utils import torch_device
label_desc: Use image node_id 2 for calling a global function with example usage: ids_tensor(shape, vocab_size, rng, name) and returns: torch