class_docstr: For training speaker recognition models. 
code: def process_config(self, config):
      data_conf = config["data"]
  
      feature_shape = data_conf["task"]["audio"].get(
          "feature_shape", None
      )
  
      if not feature_shape:
          # add feature shape, withoud batch_size
          if data_conf["task"]["suffix"] == ".npy":
              input_channels = (
                  3
                  if data_conf["task"]["audio"]["add_delta_deltas"]
                  else 1
              )
              nframe = librosa.time_to_frames(
                  data_conf["task"]["audio"]["clip_size"],
                  sr=data_conf["task"]["audio"]["sr"],
                  hop_length=data_conf["task"]["audio"]["winstep"]
                  * data_conf["task"]["audio"]["sr"],
              )
              feature_shape = [
                  nframe,
                  data_conf["task"]["audio"]["feature_size"],
                  input_channels,
              ]
          else:
              feature_shape = [
                  data_conf["task"]["audio"]["sr"]
                  * data_conf["task"]["audio"]["clip_size"]
              ]
          data_conf["task"]["audio"]["feature_shape"] = feature_shape
      logging.info(f"FEATURE SHAPE: {feature_shape}")
      return config
  
file_path: delta/delta/utils/solver/speaker_solver.py
imports: from absl import logging
  import librosa
  import delta.compat
  from delta.utils.solver.estimator_solver import EstimatorSolver
  from delta.utils.register import registers
label_desc: Use image node_id 1 for calling the SpeakerSolver obj's underlying member method code with example usage: obj.process_config(config) and returns: config