class_docstr: `FeatureConnector` for a normalized bounding box.
  
  Note: If you have multiple bounding boxes, you may want to wrap the feature
  inside a `tfds.features.Sequence`.
  
  Input:
    * `tfds.features.BBox` tuple or 4-dim `np.ndarray`.
  
  Output:
    bbox: tf.Tensor of type `tf.float32` and shape `[4,]` which contains the
      normalized coordinates of the bounding box `[ymin, xmin, ymax, xmax]`
  
  Example:
    * In the DatasetInfo object:
  
    ```
    features=features.FeatureDict({
        'bbox': tfds.features.BBox(),
    })
    ```
  
    * During generation:
  
    ```
    yield {
        'input': tfds.features.BBox(ymin=0.3, xmin=0.8, ymax=0.5, xmax=1.0),
    }
    ```
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code: def __init__(
      self,
      *,
      doc: feature_lib.DocArg = None,
  ):
      super(BBoxFeature, self).__init__(
          shape=(4,), dtype=np.float32, doc=doc
      )
  
imports: from __future__ import annotations
  from typing import Any, Union
  import numpy
  from tensorflow_datasets.core import lazy_imports_lib
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import bounding_boxes_utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import image_feature
  from tensorflow_datasets.core.features import tensor_feature
  from tensorflow_datasets.core.proto import feature_pb2
  from tensorflow_datasets.core.utils import type_utils
label_desc: Use image node_id 1 to create a new BBoxFeature object from inherited base classes: tensor_feature with example: obj = BBoxFeature()