code: def test_MultiEmbedding(kwargs):
      x = torch.randint(0, 10, size=(4, 3))
      embedding = MultiEmbedding(**kwargs)
      assert embedding.input_size == x.size(
          1
      ), "Input size should be equal to number of features"
      out = embedding(x)
      if isinstance(out, dict):
          assert isinstance(kwargs["embedding_sizes"], dict)
          for name, o in out.items():
              assert (
                  o.size(1) == embedding.output_size[name]
              ), "Output size should be equal to number of embedding dimensions"
      elif isinstance(out, torch.Tensor):
          assert isinstance(kwargs["embedding_sizes"], (tuple, list))
          assert (
              out.size(1) == embedding.output_size
          ), "Output size should be equal to number of summed embedding dimensions"
      else:
          raise ValueError(f"Unknown output type {type(out)}")
  
imports: import itertools
  import pytest
  import torch
  from pytorch_forecasting import MultiEmbedding
label_desc: Use image node_id 1 for calling a global function with example usage: test_MultiEmbedding(kwargs) without return types