code: def test_embedding(self):
      B, T, embed_size, vocab_size = 4, 10, 20, 28
  
      # create in tinygrad
      layer = Embedding(vocab_size, embed_size)
  
      with torch.no_grad():
          torch_layer = torch.nn.Embedding(
              vocab_size, embed_size
          ).eval()
          torch_layer.weight[:] = torch.tensor(
              layer.weight.numpy(), dtype=torch.float32
          )
  
      # test
      x = Tensor(np.random.randint(0, vocab_size, (B, T)))
      z = layer(x)
      torch_x = torch.tensor(x.numpy())
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(
          z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8
      )
  
      # test with empty input length
      x = Tensor(np.random.randint(0, vocab_size, (B, 0)))
      z = layer(x)
      torch_x = torch.tensor(x.numpy())
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(
          z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8
      )
  
      # test with jit enabled
      @TinyJit
      def layer_jit(x):
          return layer(x).realize()
  
      for _ in range(3):
          x = Tensor(np.random.randint(0, vocab_size, (B, T)))
          z = layer_jit(x)
          torch_x = torch.tensor(x.numpy())
          torch_z = torch_layer(torch_x)
          np.testing.assert_allclose(
              z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8
          )
  
imports: import unittest
  import numpy
  import torch
  from tinygrad import Tensor, Device, TinyJit
  from tinygrad.helpers import CI
  from tinygrad.nn import BatchNorm2d, Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, GroupNorm, LayerNorm, LayerNorm2d, Embedding, InstanceNorm
label_desc: Use image node_id 15 for calling the TestNN obj's underlying member method code with example usage: obj.test_embedding() and returns: layer