class_docstr: Meter to calculate mean AP metric for multi-label image classification task.
  
  Args:
      meters_config (AttrDict): config containing the meter settings
  
  meters_config should specify the num_classes
class_docstr_tok: ["Meter","to","calculate","mean","AP","metric","for","multi-label","image","classification","task",".","Args",":","meters_config","(","AttrDict",")",":","config","containing","the","meter","settings","meters_config","should","specify","the","num_classes"]
docstr: Set the state of meter
docstr_tok: ["Set","the","state","of","meter"]
code: def set_classy_state(self, state):
      """
      Set the state of meter
      """
      assert (
          self.name == state["name"]
      ), f"State name {state['name']} does not match meter name {self.name}"
      assert self.num_classes == state["num_classes"], (
          f"num_classes of state {state['num_classes']} "
          f"does not match object's num_classes {self.num_classes}"
      )
  
      # Restore the state -- correct_predictions and sample_count.
      self.reset()
      self._total_sample_count = state["total_sample_count"].clone()
      self._curr_sample_count = state["curr_sample_count"].clone()
      self._scores = state["scores"]
      self._targets = state["targets"]
  
imports: import logging
  import numpy
  import torch
  from classy_vision.generic.distributed_util import all_reduce_sum, gather_from_all
  from classy_vision.meters import ClassyMeter, register_meter
  from vissl.config import AttrDict
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.svm_utils.evaluate import get_precision_recall
label_desc: Use image node_id 10 for calling the MeanAPMeter obj's underlying member method code with example usage: obj.set_classy_state(state) without return types