each element.
     * **predictions** (*torch.Tensor**, **numpy.ndarray**, or
       **string/blobname*) -- The probability that an element be
       classified as true. Value should be in [0, 1]

     * **global_step** (*int*) -- Global step value to record

     * **num_thresholds** (*int*) -- Number of thresholds used to
       draw the curve.

     * **walltime** (*float*) -- Optional override default
       walltime (time.time()) seconds after epoch of event

  Examples:

     from torch.utils.tensorboard import SummaryWriter
     import numpy as np
     labels = np.random.randint(2, size=100)  # binary label
     predictions = np.random.rand(100)
     writer = SummaryWriter()
     writer.add_pr_curve('pr_curve', labels, predictions, 0)
     writer.close()

add_custom_scalars(layout)
  Create special chart by collecting charts tags in 'scalars'.
  Note that this function can only be called once for each
