code: def iter_chunk_convert_examples_to_dataset(
      task,
      examples: list,
      tokenizer,
      feat_spec: FeaturizationSpec,
      phase: str,
      verbose=False,
  ):
      for i, data_row in enumerate(
          iter_chunk_tokenize_and_featurize(
              task=task,
              examples=examples,
              tokenizer=tokenizer,
              feat_spec=feat_spec,
              phase=phase,
              verbose=verbose,
          )
      ):
          metadata = {"example_id": i}
          yield {"data_row": data_row, "metadata": metadata}
  
file_path: jiant/jiant/proj/main/preprocessing.py
imports: import numpy
  import torch
  import jiant.shared.caching
  import jiant.utils.torch_utils
  from jiant.tasks.core import FeaturizationSpec, TaskTypes
  from jiant.utils.display import maybe_tqdm, maybe_trange
label_desc: Use image node_id 5 for calling a global function with example usage: iter_chunk_convert_examples_to_dataset(task, examples, tokenizer, feat_spec, phase, verbose) without return types