code: def input_to_batch(self, item):
      item_names = [item["item_name"]]
      text = [item["text"]]
      ph = [item["ph"]]
      txt_tokens = np.int64(item["ph_token"])[None, :]
      txt_lengths = np.int64([txt_tokens.shape[1]])
      spk_ids = np.asarray(item["spk_id"], np.int64)[None]
  
      pitch_midi = np.int64(item["pitch_midi"])[
          None, : hparams["max_frames"]
      ]
      midi_dur = np.float32(item["midi_dur"])[
          None, : hparams["max_frames"]
      ]
      is_slur = np.int64(item["is_slur"])[None, : hparams["max_frames"]]
  
      batch = {
          "item_name": item_names,
          "text": text,
          "ph": ph,
          "txt_tokens": txt_tokens,
          "txt_lengths": txt_lengths,
          "spk_ids": spk_ids,
          "pitch_midi": pitch_midi,
          "midi_dur": midi_dur,
          "is_slur": is_slur,
      }
      return batch
  
imports: import os
  from collections import deque
  import librosa
  import numpy
  import onnxruntime
  from pypinyin import lazy_pinyin
  from tqdm import tqdm
  from .inference.svs.opencpop.map import cpop_pinyin2ph_func
  from .utils.hparams import hparams
  from .utils.text_encoder import TokenTextEncoder
label_desc: Use image node_id 10 for calling the Infer obj's underlying member method code with example usage: obj.input_to_batch(item) and returns: batch