class_docstr: base class for classfication 
docstr: Get prediction results for postprocessing at inference time.
  Args:
    extra_outputs: optional extra outputs from model() other than logits.
docstr_tok: ["Get","prediction","results","for","postprocessing","at","inference","time",".","Args",":","extra_outputs",":","optional","extra","outputs","from","model","(",")","other","than","logits","."]
code: def get_infer_predictions(
      self, features, softmax, alpha=None, extra_outputs=None
  ):
      """
      Get prediction results for postprocessing at inference time.
      Args:
        extra_outputs: optional extra outputs from model() other than logits.
      """
      predictions = {"softmax": softmax}
      if alpha:
          predictions.update(
              {
                  "alignment": alpha,
              }
          )
      predictions.update(features)
      predictions.pop("audio", None)
      if extra_outputs is not None:
          predictions.update(extra_outputs)
  
      logging.info("predictions: {}".format(predictions))
      return predictions
  
imports: import os
  import re
  import functools
  import collections
  from absl import logging
  import delta.compat
  from tensorflow.python import debug
  from tensorflow.python.estimator.canned import metric_keys
  from tensorboard.plugins.pr_curve import summary
  from delta import utils
  from delta.utils.hparam import HParams
  from delta.utils import metrics
  from delta.utils import summary
  from delta.utils.register import registers
  from delta.utils.solver.base_solver import ABCEstimatorSolver
label_desc: Use image node_id 11 for calling the EstimatorSolver obj's underlying member method code with example usage: obj.get_infer_predictions(features, softmax, alpha, extra_outputs) and returns: predictions