class_docstr: Priority Sort task from the Neural Turing Machines paper:
      http://arxiv.org/abs/1410.5401.
  
  See Also:
      See comments on :py:class:`~neon.data.ticker.CopyTask` class for more details.
docstr: Set up the attributes that ticker needs to see.
  
  Arguments:
      seq_len_max (int): Longest allowable sequence length
      vec_size (int): Width of the bit-vector to be copied (this was 8 in paper)
docstr_tok: ["Set","up","the","attributes","that","ticker","needs","to","see",".","Arguments",":","seq_len_max","(","int",")",":","Longest","allowable","sequence","length","vec_size","(","int",")",":","Width","of","the","bit-vector","to","be","copied","(","this","was","8","in","paper",")"]
code: def __init__(self, seq_len_max, vec_size):
      """
      Set up the attributes that ticker needs to see.
  
      Arguments:
          seq_len_max (int): Longest allowable sequence length
          vec_size (int): Width of the bit-vector to be copied (this was 8 in paper)
      """
  
      self.seq_len_max = seq_len_max
      self.vec_size = vec_size
      self.nout = (
          self.vec_size
      )  # we output the sorted sequence, with no stop bit
      self.nin = (
          self.vec_size + 3
      )  # extra channels for start, stop, and priority
  
      # seq is seen once as input with start and stop bits
      # then we output seq in sorted order
      self.time_steps_func = lambda l: 2 * l + 2
      self.time_steps_max = self.time_steps_func(self.seq_len_max)
      self.max_columns = self.time_steps_max * self.be.bsz
  
imports: import numpy
  from neon import NervanaObject
label_desc: Use image node_id 1 to create a new PrioritySortTask object from inherited base classes: Task with example: obj = PrioritySortTask(seq_len_max, vec_size)