class_docstr: DatasetBuilder for penguins dataset.
class_docstr_tok: ["DatasetBuilder","for","penguins","dataset","."]
docstr: Returns the dataset metadata.
docstr_tok: ["Returns","the","dataset","metadata","."]
code: def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
      supervised_keys = None
      features = self.builder_config.features
      supervised_features = features.copy()
      label_name = self.builder_config.label
  
      if self.builder_config.name == "processed":
          label_feature = supervised_features.pop(label_name, None)
          supervised_keys = ("features", label_name)
          features = {
              label_name: label_feature,
              "features": tfds.features.Tensor(
                  shape=(len(supervised_features),),
                  dtype=next(iter(supervised_features.values())),
              ),
          }
      elif self.builder_config.name == "simple":
          supervised_feature_names = {
              key: key for key in supervised_features.keys()
          }
          supervised_keys = (supervised_feature_names, label_name)
  
      return self.dataset_info_from_configs(
          features=tfds.features.FeaturesDict(features),
          supervised_keys=supervised_keys,
          homepage="https://allisonhorst.github.io/palmerpenguins/",
      )
  
file_path: datasets/tensorflow_datasets/datasets/penguins/penguins_dataset_builder.py
imports: from __future__ import annotations
  import csv
  import dataclasses
  import textwrap
  import typing
  from typing import Any, Mapping, Optional, Union
  import numpy
  import tensorflow_datasets.core.utils
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the Builder obj's underlying member method code with example usage: obj._info() and returns: self