code: def generate_examples(
      features: dict, num_examples=100, seq_shapes=None
  ):
      dummy_data = []
      seq_shapes = seq_shapes or {}
      for i in range(num_examples):
          example = {}
          for col_id, (k, v) in enumerate(features.items()):
              if isinstance(v, _ArrayXD):
                  if k == "dynamic":
                      first_dim = random.randint(1, 3)
                      data = np.random.rand(
                          first_dim, *v.shape[1:]
                      ).astype(v.dtype)
                  else:
                      data = np.random.rand(*v.shape).astype(v.dtype)
              elif isinstance(v, datasets.Value):
                  data = "foo"
              elif isinstance(v, datasets.Sequence):
                  while isinstance(v, datasets.Sequence):
                      v = v.feature
                  shape = seq_shapes[k]
                  data = np.random.rand(*shape).astype(v.dtype)
              example[k] = data
              dummy_data.append((i, example))
  
      return dummy_data
  
imports: import os
  import random
  import tempfile
  import unittest
  import numpy
  import pandas
  import pyarrow
  import pytest
  from absl.testing import parameterized
  import datasets
  from datasets.arrow_writer import ArrowWriter
  from datasets.features import Array2D, Array3D, Array4D, Array5D, Value
  from datasets.features.features import Array3DExtensionType, PandasArrayExtensionDtype, _ArrayXD
  from datasets.formatting.formatting import NumpyArrowExtractor, SimpleArrowExtractor
label_desc: Use image node_id 1 for calling a global function with example usage: generate_examples(features, num_examples, seq_shapes) and returns: dummy_data