code: def map_to_pred(batch):
      input_values = processor(
          batch["speech"], return_tensors="pt", padding="longest"
      ).input_values
      with torch.no_grad():
          logits = model(input_values.to(f"cuda:{local_rank}")).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.batch_decode(predicted_ids)
      batch["transcription"] = transcription
      return batch
  
file_path: deepspeedexamples/inference/huggingface/automatic-speech-recognition/test-wav2vec2.py
imports: from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import soundfile
  import torch
  from jiwer import wer
  import os
  import deepspeed
  from deepspeed import module_inject
  from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2EncoderLayer
label_desc: Use image node_id 2 for calling a global function with example usage: map_to_pred(batch) and returns: batch