code: def get_dummy_data(
      n_samples, n_tags, n_vocab, length_mean, length_variance
  ):
      Xs = []
      Ys = []
      for _ in range(n_samples):
          length = (
              numpy.random.normal(size=1, scale=length_variance)
              + length_mean
          )
          shape = (max(1, int(length)),)
          X = numpy.random.uniform(0, n_vocab - 1, shape)
          Y = numpy.random.uniform(0, n_tags - 1, shape)
          assert X.size, length
          assert Y.size, length
          Xs.append(X.reshape((-1, 1)).astype("i"))
          Ys.append(to_categorical(Y.astype("i")))
      return Xs, Ys
  
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 2 for calling a global function with example usage: get_dummy_data(n_samples, n_tags, n_vocab, length_mean, length_variance) and returns: Xs,  Ys