code: def get_dummy_metadata():
      distribution = [
          mlc.FileObject(
              name="raw_data",
              description="File with the data.",
              encoding_format="application/jsonlines",
              content_url="data/raw_data.jsonl",
              sha256="sha256",
          ),
      ]
      record_sets = [
          mlc.RecordSet(
              name="jsonl",
              fields=[
                  mlc.Field(
                      name="index",
                      description="The sample index.",
                      data_types=mlc.DataType.INTEGER,
                      source=mlc.Source(
                          uid="raw_data",
                          node_type="distribution",
                          extract=mlc.Extract(column="index"),
                      ),
                  ),
                  mlc.Field(
                      name="text",
                      description="The dummy sample text.",
                      data_types=mlc.DataType.TEXT,
                      source=mlc.Source(
                          uid="raw_data",
                          node_type="distribution",
                          extract=mlc.Extract(column="text"),
                      ),
                  ),
              ],
          )
      ]
      dummy_metadata = mlc.Metadata(
          name="DummyDataset",
          description="Dummy description.",
          conforms_to="http://mlcommons.org/croissant/1.0",
          citation=(
              "@article{dummyarticle, title={title}, author={author}, year={2020}}"
          ),
          url="https://dummy_url",
          distribution=distribution,
          record_sets=record_sets,
          version="1.2.0",
          license="Public",
      )
      return dummy_metadata
  
file_path: datasets/tensorflow_datasets/core/dataset_builders/croissant_builder_test.py
imports: import json
  import tempfile
  from etils import epath
  import numpy
  import pytest
  from tensorflow_datasets import testing
  from tensorflow_datasets.core import FileFormat
  from tensorflow_datasets.core.dataset_builders import croissant_builder
  from tensorflow_datasets.core.features import image_feature
  from tensorflow_datasets.core.features import text_feature
  from tensorflow_datasets.core.utils.lazy_imports_utils import mlcroissant
label_desc: Use image node_id 1 for calling a global function with example usage: get_dummy_metadata() and returns: dummy_metadata