class_docstr: Target assigner to compute classification and regression targets.
class_docstr_tok: ["Target","assigner","to","compute","classification","and","regression","targets","."]
docstr: Sets the static shape of the target.
  
  Args:
    target: the target tensor. Its first dimension will be overwritten.
    num_anchors: the number of anchors, which is used to override the target's
      first dimension.
  
  Returns:
    A tensor with the shape info filled in.
docstr_tok: ["Sets","the","static","shape","of","the","target",".","Args",":","target",":","the","target","tensor",".","Its","first","dimension","will","be","overwritten",".","num_anchors",":","the","number","of","anchors",",","which","is","used","to","override","the","target's","first","dimension",".","Returns",":","A","tensor","with","the","shape","info","filled","in","."]
code: def _reset_target_shape(self, target, num_anchors):
      """Sets the static shape of the target.
  
      Args:
        target: the target tensor. Its first dimension will be overwritten.
        num_anchors: the number of anchors, which is used to override the target's
          first dimension.
  
      Returns:
        A tensor with the shape info filled in.
      """
      target_shape = target.get_shape().as_list()
      target_shape[0] = num_anchors
      target.set_shape(target_shape)
      return target
  
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 4 for calling the TargetAssigner obj's underlying member method code with example usage: obj._reset_target_shape(target, num_anchors) and returns: target