code: def main(path: Optional[Path] = None, out_dir: Optional[Path] = None):
      if prefer_gpu():
          print("Using gpu!")
          use_pytorch_for_gpu_memory()
      # You can edit the CONFIG string within the file, or copy it out to
      # a separate file and pass in the path.
      if path is None:
          config = Config().from_str(CONFIG)
      else:
          config = Config().from_disk(path)
      # resolve constructs objects whenever you have blocks with an @ key.
      # In the optimizer block we write @optimizers = "Adam.v1". This tells Thinc
      # to use registry.optimizers to fetch the "Adam.v1" function. You can
      # register your own functions as well and build up trees of objects.
      C = thinc.registry.resolve(config)
      words_per_subbatch = C["training"]["words_per_subbatch"]
      n_epoch = C["training"]["n_epoch"]
      batch_size = C["training"]["batch_size"]
      model = C["model"]
      optimizer = C["optimizer"]
      calculate_loss = SequenceCategoricalCrossentropy()
  
      (train_X, train_Y), (
          dev_X,
          dev_Y,
      ) = ml_datasets.ud_ancora_pos_tags()
      # Convert the outputs to cupy (if we're using that)
      train_Y = list(map(model.ops.asarray, train_Y))
      dev_Y = list(map(model.ops.asarray, dev_Y))
      # Pass in a small batch of data, to fill in missing shapes
      model.initialize(X=train_X[:5], Y=train_Y[:5])
      for epoch in range(n_epoch):
          # Transformers often learn best with large batch sizes -- larger than
          # fits in GPU memory. But you don't have to backprop the whole batch
          # at once. Here we consider the "logical" batch size (number of examples
          # per update) separately from the physical batch size.
          batches = model.ops.multibatch(
              batch_size, train_X, train_Y, shuffle=True
          )
          for outer_batch in tqdm.tqdm(batches, leave=False):
              # For the physical batch size, what we care about is the number
              # of words (considering padding too). We also want to sort by
              # length, for efficiency.
              for batch in minibatch_by_words(
                  outer_batch, words_per_subbatch
              ):
                  inputs, truths = zip(*batch)
                  guesses, backprop = model(inputs, is_train=True)
                  backprop(calculate_loss.get_grad(guesses, truths))
              # At the end of the batch, we call the optimizer with the accumulated
              # gradients, and advance the learning rate schedules.
              model.finish_update(optimizer)
              optimizer.step_schedules()
          # You might want to evaluate more often than once per epoch; that's up
          # to you.
          score = evaluate_sequences(model, dev_X, dev_Y, 128)
          print(epoch, f"{score:.3f}")
          if out_dir:
              model.to_disk(out_dir / f"{epoch}.bin")
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Tuple, Callable
  import torch
  from pathlib import Path
  from transformers import AutoTokenizer, AutoModel
  import thinc
  from thinc.api import PyTorchWrapper, Softmax, chain, with_array, Model, Config
  from thinc.api import torch2xp, xp2torch, SequenceCategoricalCrossentropy
  from thinc.api import prefer_gpu, use_pytorch_for_gpu_memory
  from thinc.types import Floats2d, ArgsKwargs
  import ml_datasets
  import tqdm
  import typer
label_desc: Use image node_id 1 for calling a global function with example usage: main(path, out_dir) without return types