class_docstr: Target assigner to compute classification and regression targets.
class_docstr_tok: ["Target","assigner","to","compute","classification","and","regression","targets","."]
docstr: Returns the default target for anchors to regress to.
  
  Default regression targets are set to zero (though in
  this implementation what these targets are set to should
  not matter as the regression weight of any box set to
  regress to the default target is zero).
  
  Returns:
    default_target: a float32 tensor with shape [1, box_code_dimension]
docstr_tok: ["Returns","the","default","target","for","anchors","to","regress","to",".","Default","regression","targets","are","set","to","zero","(","though","in","this","implementation","what","these","targets","are","set","to","should","not","matter","as","the","regression","weight","of","any","box","set","to","regress","to","the","default","target","is","zero",")",".","Returns",":","default_target",":","a","float32","tensor","with","shape","[","1",",","box_code_dimension","]"]
code: def _default_regression_target(self):
      """Returns the default target for anchors to regress to.
  
      Default regression targets are set to zero (though in
      this implementation what these targets are set to should
      not matter as the regression weight of any box set to
      regress to the default target is zero).
  
      Returns:
        default_target: a float32 tensor with shape [1, box_code_dimension]
      """
      return tf.constant([self._box_coder.code_size * [0]], tf.float32)
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/pipeline/anchors_utils/target_assigner.py
imports: import tensorflow.compat.v1
  from .None import box_list
  from .None import shape_utils
label_desc: Use image node_id 6 for calling the TargetAssigner obj's underlying member method code with example usage: obj._default_regression_target() and returns: tf