class_docstr: `FeatureConnector` for camera calibration (extrinsic and intrinsic).
  
  During `_generate_examples`, the feature connector accepts as input:
  
    * `parameter_dict:` A dictionary containing the extrinsic and instrinsic
    parameters of the camera as:
      - 'pose': Dictionary containing
          * Either 3x3 rotation matrix and translation vector:
            {
              'R': A `float32` tensor with shape `[3, 3]` denoting the
                   3D rotation matrix.
              't': A `float32` tensor with shape `[3,]` denoting the
                   translation vector.
            }
      OR
         * look_at, position and up-vector:
            {
              'look_at': float32 vector of shape (3,).
              'position': float32 vector of shape (3,).
              'up': float32 vector of shape (3,).
            }
      - 'f': focal length of the camera in pixel (either single float32 value
      or tuple of float32 as (f_x, f_y).
      - 'optical_center': Optical center of the camera
      in pixel coordinates as tuple (c_x, c_y)
      Optional parameters:
      - 'skew': float32 denoting the skew of the camera axes.
      - 'aspect_ratio': float32 denoting the aspect_ratio,
      if single fixed focal length is provided.
  
  
  Output:
    A dictionary containing:
  
    * 'pose': A `tensorflow_graphics.datasets.features.Pose` FeatureConnector
    representing the 3D pose of the camera.
    * 'intrinsics': A `float32` tensor with shape `[3,3]` denoting the intrinsic
    matrix.
  
  Example:
    Default values for skew (s) and aspect_ratio(a) are 0 and 1, respectively.
  
    Full calibration matrix:
      K = [[ f_x,   s, c_x ],
           [   0, f_y, c_y ],
           [   0,   0,  1  ]]
  
    With same focal length:
      K = [[ f,  s, c_x ],
           [ 0, af, c_y ],
           [ 0,  0,  1  ]]
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code: def from_json_content(cls, value) -> "Camera":
      return cls()
  
file_path: graphics/tensorflow_graphics/datasets/features/camera_feature.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tensorflow_datasets import features
  from tensorflow_graphics.datasets.features import pose_feature
label_desc: Use image node_id 5 for calling the Camera obj's underlying member method code with example usage: obj.from_json_content(cls, value) and returns: cls