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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def example_protos_from_path(path, num_examples=10, start_index=0, parse_examples=True, sampling_odds=1, example_class=tf.train.Example):
 """Returns a number of examples from the provided path. Args: path: A string path to the examples. num_examples: The maximum number of examples to return from the path. parse_examples: If true then parses the serialized proto from the path into proto objects. Defaults to True. sampling_odds: Odds of loading an example, used for sampling. When >= 1 (the default), then all examples are loaded. example_class: tf.train.Example or tf.train.SequenceExample class to load. Defaults to tf.train.Example. Returns: A list of Example protos or serialized proto strings at the path. Raises: InvalidUserInputError: If examples cannot be procured from the path. """


  def append_examples_from_iterable(iterable, examples):
    for value in iterable:
      if sampling_odds >= 1 or random.random() < sampling_odds:
        examples.append(
            example_class.FromString(value) if parse_examples else value)
        if len(examples) >= num_examples:
          return

  examples = []

  if path.endswith('.csv'):
    def are_floats(values):
      for value in values:
        try:
          float(value)
        except ValueError:
          return False
      return True
    csv.register_dialect('CsvDialect', skipinitialspace=True)
    rows = csv.DictReader(open(path), dialect='CsvDialect')
    for row in rows:
      if sampling_odds < 1 and random.random() > sampling_odds:
        continue
      example = tf.train.Example()
      for col in row.keys():
          # Parse out individual values from vertical-bar-delimited lists
          values = [val.strip() for val in row[col].split('|')]
          if are_floats(values):
            example.features.feature[col].float_list.value.extend(
              [float(val) for val in values])
          else:
            example.features.feature[col].bytes_list.value.extend(
              [val.encode('utf-8') for val in values])
      examples.append(
        example if parse_examples else example.SerializeToString())
      if len(examples) >= num_examples:
        break
    return examples

  filenames = filepath_to_filepath_list(path)
  compression_types = [
      '',  # no compression (distinct from `None`!)
      'GZIP',
      'ZLIB',
  ]
  current_compression_idx = 0
  current_file_index = 0
  while (current_file_index < len(filenames) and
         current_compression_idx < len(compression_types)):
    try:
      record_iterator = tf.compat.v1.python_io.tf_record_iterator(
          path=filenames[current_file_index],
          options=tf.io.TFRecordOptions(
              compression_types[current_compression_idx]))
      append_examples_from_iterable(record_iterator, examples)
      current_file_index += 1
      if len(examples) >= num_examples:
        break
    except tf.errors.DataLossError:
      current_compression_idx += 1
    except (IOError, tf.errors.NotFoundError) as e:
      raise common_utils.InvalidUserInputError(e)

  if examples:
    return examples
  else:
    raise common_utils.InvalidUserInputError(
        'No examples found at ' + path +
        '. Valid formats are TFRecord files.')