class_docstr: The APMeter measures the average precision per class.
  
  The APMeter is designed to operate on `NxK` Tensors `output` and
  `target`, and optionally a `Nx1` Tensor weight where (1) the `output`
  contains model output scores for `N` examples and `K` classes that ought to
  be higher when the model is more convinced that the example should be
  positively labeled, and smaller when the model believes the example should
  be negatively labeled (for instance, the output of a sigmoid function); (2)
  the `target` contains only values 0 (for negative examples) and 1
  (for positive examples); and (3) the `weight` ( > 0) represents weight for
  each sample.
docstr: Resets the meter with empty member variables
docstr_tok: ["Resets","the","meter","with","empty","member","variables"]
code: def reset(self):
      """Resets the meter with empty member variables"""
      self.scores = torch.FloatTensor(torch.FloatStorage())
      self.targets = torch.LongTensor(torch.LongStorage())
      self.weights = torch.FloatTensor(torch.FloatStorage())
  
file_path: pywick/pywick/meters/apmeter.py
imports: import math
  from .None import meter
  import torch
label_desc: Use image node_id 2 for calling the APMeter obj's underlying member method code with example usage: obj.reset() without return types