code: def equal_distribution(
      tiny_func,
      torch_func=None,
      numpy_func=None,
      shape=(20, 23),
      alpha=0.05,
  ):
      Tensor.manual_seed(1337)
      torch.manual_seed(1337)
      np.random.seed(1337)
      assert not (
          torch_func is None and numpy_func is None
      ), "no function to compare with"
      x1 = tiny_func(*shape).numpy().flatten()
      x2 = tiny_func(shape).numpy().flatten()
      if numpy_func is not None:
          y = numpy_func(shape).flatten()
      if torch_func is not None:
          z = torch_func(shape).numpy().flatten()
      return (
          numpy_func is None
          or (kstest(x1, y) >= alpha and kstest(x2, y) >= alpha)
      ) and (
          torch_func is None
          or (kstest(x1, z) >= alpha and kstest(x2, z) >= alpha)
      )
  
imports: import math
  import unittest
  import numpy
  import torch
  from tinygrad import nn, dtypes, Tensor
  from functools import partial
label_desc: Use image node_id 3 for calling a global function with example usage: equal_distribution(tiny_func, torch_func, numpy_func, shape, alpha) and returns: unknown