class_docstr: Attention net. See file docstring.
class_docstr_tok: ["Attention","net",".","See","file","docstring","."]
docstr: Parses the shift string.
  
  The hparams should contain the key shift_values, which maps to a
  comma-separated string of integers. These integers specify the number of
  timesteps to predict/reconstruct to compute auxiliary losses.
  
  For instance, "-4,2,6" means to reconstruct the target 4 steps before and
  predict the targets 2 steps and 6 steps ahead.
  
  Returns:
    List of int != 0 shift values to compute the auxiliary losses.
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code: def _extract_shift_values(self):
      """Parses the shift string.
  
      The hparams should contain the key shift_values, which maps to a
      comma-separated string of integers. These integers specify the number of
      timesteps to predict/reconstruct to compute auxiliary losses.
  
      For instance, "-4,2,6" means to reconstruct the target 4 steps before and
      predict the targets 2 steps and 6 steps ahead.
  
      Returns:
        List of int != 0 shift values to compute the auxiliary losses.
      """
      shift_values_str = self._hparams.get("shift_values", "")
      shift_values = [int(x) for x in shift_values_str.split(",")]
  
      tf.logging.info(
          "Computing auxiliary losses for the following shifts: %s",
          shift_values,
      )
  
      return shift_values
  
file_path: tensor2tensor/tensor2tensor/models/research/transformer_aux.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling the TransformerAux obj's underlying member method code with example usage: obj._extract_shift_values() and returns: shift_values