code: def main(
      numpy: bool = False,
      pytorch: bool = False,
      generic: bool = False,
      gpu_id: int = -1,
  ):
      global CONFIG
      fix_random_seed(0)
      if gpu_id >= 0:
          require_gpu(gpu_id)
          print("Set GPU", gpu_id)
      backends = {
          "pytorch": pytorch,
          "numpy": numpy,
          "generic": generic,
      }
      for name, use_backend in backends.items():
          if not use_backend:
              print(f"Skipping {name}")
              continue
          set_backend(name, gpu_id)
          print("Getting data")
          C = registry.resolve(Config().from_str(CONFIG))
          model = C["model"]
          X, Y = get_dummy_data(**C["data"])
          print("Copy to device")
          X = [model.ops.asarray(x) for x in X]
          Y = [model.ops.asarray(y) for y in Y]
          print("Begin init", len(X))
          model.initialize(X=X[:5])
          print("Pre-batch")
          n_words = sum(len(x) for x in X)
          batches = model.ops.multibatch(16, X, Y)
          batches = [
              (model.layers[0].predict(x), y) for x, y in batches
          ]
          model.layers.pop(0)
          print("Start")
          start_time = timer()
          total = run_forward(model, [x for x, y in batches])
          end_time = timer()
          print(name, n_words, total, end_time - start_time)
          start_time = timer()
          total = run_forward_backward(model, batches)
          end_time = timer()
          print(name, n_words, total, end_time - start_time)
  
file_path: thinc/examples/benchmarks/lstm_tagger.py
imports: from typing import List
  import typer
  import tqdm
  import numpy.random
  from timeit import default_timer
  from thinc.api import Model, Config, registry
  from thinc.api import chain, list2padded, with_array, with_padded
  from thinc.api import to_categorical, set_current_ops
  from thinc.api import Ops, NumpyOps, CupyOps, fix_random_seed, require_gpu
  from thinc.types import Ints1d, Ints2d, Floats2d, Padded
label_desc: Use image node_id 6 for calling a global function with example usage: main(numpy, pytorch, generic, gpu_id) without return types