class_docstr: `FeatureConnector` for 3d pose representations.
  
  During `_generate_examples`, the feature connector accepts as input any of:
  
    * `dict:` A dictionary containing the rotation and translation of the
    object (see output format below).
  
  Output:
    A dictionary containing:
  
    * 'R': A `float32` tensor with shape `[3, 3]` denoting the
    3D rotation matrix.
    * 't': A `float32` tensor with shape `[3,]` denoting the
    translation vector.
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code: def to_json_content(self):
      return {}
  
file_path: graphics/tensorflow_graphics/datasets/features/pose_feature.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_datasets import features
label_desc: Use image node_id 4 for calling the Pose obj's underlying member method code with example usage: obj.to_json_content() and returns: dict