class_docstr: Bigearthnet remote sensing dataset of Sentinel-2 image patches.
class_docstr_tok: ["Bigearthnet","remote","sensing","dataset","of","Sentinel-2","image","patches","."]
code: def _info(self):
      metadata_dict = tfds.features.FeaturesDict(
          {
              "acquisition_date": tfds.features.Text(),
              "coordinates": {
                  "lrx": np.int64,
                  "lry": np.int64,
                  "ulx": np.int64,
                  "uly": np.int64,
              },
              "projection": tfds.features.Text(),
              "tile_source": tfds.features.Text(),
          }
      )
      if self.builder_config.selection == "rgb":
          features = tfds.features.FeaturesDict(
              {
                  "image": tfds.features.Image(shape=[120, 120, 3]),
                  "labels": tfds.features.Sequence(
                      tfds.features.ClassLabel(names=_LABELS)
                  ),
                  "filename": tfds.features.Text(),
                  "metadata": metadata_dict,
              }
          )
          supervised_keys = ("image", "labels")
      elif self.builder_config.selection == "all":
          features = tfds.features.FeaturesDict(
              {
                  "B01": tfds.features.Tensor(
                      shape=[20, 20], dtype=np.float32
                  ),
                  "B02": tfds.features.Tensor(
                      shape=[120, 120], dtype=np.float32
                  ),
                  "B03": tfds.features.Tensor(
                      shape=[120, 120], dtype=np.float32
                  ),
                  "B04": tfds.features.Tensor(
                      shape=[120, 120], dtype=np.float32
                  ),
                  "B05": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "B06": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "B07": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "B08": tfds.features.Tensor(
                      shape=[120, 120], dtype=np.float32
                  ),
                  "B09": tfds.features.Tensor(
                      shape=[20, 20], dtype=np.float32
                  ),
                  "B11": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "B12": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "B8A": tfds.features.Tensor(
                      shape=[60, 60], dtype=np.float32
                  ),
                  "labels": tfds.features.Sequence(
                      tfds.features.ClassLabel(names=_LABELS)
                  ),
                  "filename": tfds.features.Text(),
                  "metadata": metadata_dict,
              }
          )
          supervised_keys = None
  
      return self.dataset_info_from_configs(
          features=features,
          supervised_keys=supervised_keys,
          homepage="http://bigearth.net",
      )
  
file_path: datasets/tensorflow_datasets/datasets/bigearthnet/bigearthnet_dataset_builder.py
imports: from __future__ import annotations
  import io
  import json
  import os
  import numpy
  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