for i in range(100):
             label_img[i]*=i/100.0
     writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
     writer.add_embedding(torch.randn(100, 5), label_img=label_img)
     writer.add_embedding(torch.randn(100, 5), metadata=meta)

add_pr_curve(tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None)
  Adds precision recall curve. Plotting a precision-recall curve
  lets you understand your model's performance under different
  threshold settings. With this function, you provide the ground
  truth labeling (T/F) and prediction confidence (usually the
  output of your model) for each target. The TensorBoard UI will
  let you choose the threshold interactively.

  Parameters:
     * **tag** (*str*) -- Data identifier

     * **labels** (*torch.Tensor**, **numpy.ndarray**, or
       **string/blobname*) -- Ground truth data. Binary label for
       each element.
