class_docstr: Emotion Task 
docstr: without batch_size
docstr_tok: ["without","batch_size"]
code: def feat_output_shape(config):
      """without batch_size"""
      if (
          "feature_shape" in config["task"]["audio"]
          and config["task"]["audio"]["feature_shape"]
      ):
          return config["task"]["audio"]["feature_shape"]
  
      if config["task"]["suffix"] == ".npy":
          input_channels = (
              3 if config["task"]["audio"]["add_delta_deltas"] else 1
          )
          nframe = librosa.time_to_frames(
              config["task"]["audio"]["clip_size"],
              sr=config["task"]["audio"]["sr"],
              hop_length=config["task"]["audio"]["winstep"]
              * config["task"]["audio"]["sr"],
          )
          feature_shape = [
              nframe,
              config["task"]["audio"]["feature_size"],
              input_channels,
          ]
      else:
          feature_shape = [
              config["task"]["audio"]["sr"]
              * config["task"]["audio"]["clip_size"]
          ]
      config["task"]["audio"]["feature_shape"] = feature_shape
      return feature_shape
  
imports: import re
  import ast
  import os
  import copy
  import threading
  import itertools
  import functools
  import random
  from collections import defaultdict
  import librosa
  import numpy
  import pandas
  import delta.compat
  from absl import logging
  from sklearn.model_selection import train_test_split
  from delta import utils
  from delta.data import feat
  from delta.utils.register import registers
  from delta.data.task.base_speech_task import SpeechTask
label_desc: Use image node_id 10 for calling the SpeechClsTask obj's underlying member method code with example usage: obj.feat_output_shape(config) and returns: feature_shape, config