code: def make_dataset(seed=42):
      rng = np.random.RandomState(seed)
      N = 100
      D = 2
  
      X = rng.randn(N, D) * 2
  
      # center the first N/2 points at (-2,-2)
      mid = N // 2
      X[:mid, :] = X[:mid, :] - 2 * np.ones((mid, D))
  
      # center the last N/2 points at (2, 2)
      X[mid:, :] = X[mid:, :] + 2 * np.ones((mid, D))
  
      # labels: first N/2 are 0, last N/2 are 1
      Y = np.array([0] * mid + [1] * mid).reshape(100, 1)
  
      x = torch.Tensor(X)
      y = torch.Tensor(Y)
      return x, y
  
file_path: pytorch-optimizer/tests/test_optimizer_with_nn.py
imports: import numpy
  import pytest
  import torch
  from torch import nn
  import torch_optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(seed) and returns: x,  y