class_docstr: Speech Emotion Solver base on Estimator
class_docstr_tok: ["Speech","Emotion","Solver","base","on","Estimator"]
docstr: infer input pipeline 
docstr_tok: ["infer","input","pipeline"]
code: def create_serving_input_receiver_fn(self):
      """infer input pipeline"""
      # with batch_size
      taskconf = self.config["data"]["task"]
      shape = [None] + taskconf["audio"]["feature_shape"]
      logging.debug("serving input shape:{}".format(shape))
  
      # pylint: disable=no-member
      return tf.estimator.export.build_raw_serving_input_receiver_fn(
          features={
              "inputs": tf.placeholder(
                  name="inputs", shape=shape, dtype=tf.float32
              ),
              "texts": tf.placeholder(
                  name="texts",
                  shape=(None, taskconf["text"]["max_text_len"]),
                  dtype=tf.int32,
              ),
          },
          default_batch_size=None,
      )
  
imports: import librosa
  from absl import logging
  import delta.compat
  from tensorflow.keras import backend
  from tensorflow.python.keras.utils import losses_utils
  from delta import utils
  from delta.utils.solver.estimator_solver import EstimatorSolver
  from delta.utils.solver.utils.callbacks import ClassReportCallBack
  from delta.utils.solver.asr_solver import AsrSolver
  from delta.utils.register import registers
label_desc: Use image node_id 3 for calling the EmotionSolver obj's underlying member method code with example usage: obj.create_serving_input_receiver_fn() and returns: tf