class_docstr: Model using ResNet50 to predict single 3D oriented bounding box.
docstr: Decode batch and place into inputs dict.
  
  Args:
    batch: The input batch.
    inputs: The dict to put the decoded batch.
  
  Returns:
    Dict containing decoded input batch.
docstr_tok: ["Decode","batch","and","place","into","inputs","dict",".","Args",":","batch",":","The","input","batch",".","inputs",":","The","dict","to","put","the","decoded","batch",".","Returns",":","Dict","containing","decoded","input","batch","."]
code: def decode_batch(batch, inputs):
      """Decode batch and place into inputs dict.
  
      Args:
        batch: The input batch.
        inputs: The dict to put the decoded batch.
  
      Returns:
        Dict containing decoded input batch.
      """
      inputs["filename"] = batch[0]
      inputs["image"] = batch[1]
      inputs["image_size"] = batch[2]
      inputs["center2d"] = batch[3]
      inputs["center3d"] = batch[4]
      inputs["box_dim2d"] = batch[5]
      inputs["box_dim3d"] = batch[6]
      inputs["rotation"] = batch[7]
      inputs["rt"] = tf.reshape(batch[9], [-1, 3, 4])
      inputs["k"] = tf.reshape(batch[10], [-1, 3, 3])
  
imports: import math
  import tensorflow
label_desc: Use image node_id 4 for calling the SingleObjectModel obj's underlying member method code with example usage: obj.decode_batch(batch, inputs) without return types