code: def _parse_examples(
      examples_file_content: str,
  ) -> Sequence[_IndexedExample]:
      """Returns the examples in the given examples file content."""
      dataset = csv.DictReader(
          examples_file_content.splitlines(), delimiter="\t"
      )
      examples = []
      for i, row in enumerate(dataset):
          path = row.get("path", None)
          if not path:
              continue
          try:
              example = {
                  "client_id": row["client_id"],
                  "sentence": row["sentence"],
                  "upvotes": int(row["up_votes"])
                  if row["up_votes"]
                  else 0,
                  "downvotes": int(row["down_votes"])
                  if row["down_votes"]
                  else 0,
                  "age": row["age"],
                  "gender": row["gender"] if row["gender"] else -1,
                  "accent": row["accent"],
                  "segment": row["segment"],
              }
              examples.append(
                  _IndexedExample(
                      index=i, example=example, clip_filename=path
                  )
              )
          except KeyError as e:
              raise ValueError(f"Could not parse row: {row}") from e
      return examples
  
imports: import csv
  import dataclasses
  import functools
  import io
  import re
  from typing import Any, Mapping, Sequence
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling a global function with example usage: _parse_examples(examples_file_content) and returns: examples