class_docstr: DatasetBuilder for segment_anything dataset.
docstr: Returns the dataset metadata.
docstr_tok: ["Returns","the","dataset","metadata","."]
code: def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
      annotation_feature = tfds.features.FeaturesDict(
          {
              "area": tfds.features.Scalar(
                  dtype=np.uint64, doc="The area in pixels of the mask."
              ),
              "bbox": tfds.features.BBoxFeature(
                  doc="The box around the mask, in TFDS format."
              ),
              "crop_box": tfds.features.BBoxFeature(
                  doc=(
                      "The crop of the image used to generate the mask,"
                      " in TFDS format."
                  )
              ),
              "id": tfds.features.Scalar(
                  dtype=np.uint64, doc="Identifier for the annotation."
              ),
              "point_coords": tfds.features.Tensor(
                  shape=(1, 2),
                  dtype=np.float64,
                  doc=(
                      "The point coordinates input to the model to generate the mask."
                  ),
              ),
              "predicted_iou": tfds.features.Scalar(
                  dtype=np.float64,
                  doc="The model's own prediction of the mask's quality.",
              ),
              "segmentation": tfds.features.FeaturesDict(
                  {
                      "counts": np.str_,
                      "size": tfds.features.Tensor(
                          shape=(2,), dtype=np.uint64
                      ),
                  },
                  doc=(
                      "Encoded segmentation mask in COCO RLE format (dict"
                      " with keys `size` and `counts`)."
                  ),
              ),
              "stability_score": tfds.features.Scalar(
                  dtype=np.float64,
                  doc="A measure of the mask's quality.",
              ),
          }
      )
      return self.dataset_info_from_configs(
          features=tfds.features.FeaturesDict(
              {
                  "image": tfds.features.FeaturesDict(
                      {
                          "content": tfds.features.Image(
                              doc="Content of the image."
                          ),
                          "file_name": np.str_,
                          "height": np.uint64,
                          "image_id": np.uint64,
                          "width": np.uint64,
                      }
                  ),
                  "annotations": tfds.features.Sequence(
                      annotation_feature
                  ),
              }
          ),
          homepage=_HOMEPAGE_URL,
          license=f"Custom (see: {_HOMEPAGE_URL})",
      )
  
file_path: datasets/tensorflow_datasets/datasets/segment_anything/segment_anything_dataset_builder.py
imports: import csv
  import json
  from typing import Dict, List
  from etils import epath
  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