code: def move_to(x, device: torch.device):
      if hasattr(x, "to"):
          return x.to(device)
      if isinstance(x, list) or isinstance(x, tuple):
          return [move_to(i, device) for i in x]
      if isinstance(x, dict):
          return {k: move_to(v, device) for k, v in x.items()}
      return x
  
file_path: textlesslib/examples/speaker_probing/train.py
imports: import torch
  import argparse
  from textless.data.quantized_datasets import QuantizedLibriSpeech
  from torch.utils.data import DataLoader
  import torch.nn.functional
  from probes import ContinuousClassifier, DiscreteClassifier, ConstantBaseline
  from textless.data.speech_encoder import SpeechEncoder
  from textless import dispatch_dense_model, dispatch_quantizer
label_desc: Use image node_id 2 for calling a global function with example usage: move_to(x, device) and returns: x, x, unknown, unknown