code: def test_embedding(
      *,
      dtypes_indices_weights,
      max_norm,
      test_flags,
      backend_fw,
      on_device,
      fn_name,
  ):
      dtypes, indices, weights, _ = dtypes_indices_weights
      dtypes = [dtypes[1], dtypes[0]]
  
      helpers.test_function(
          input_dtypes=dtypes,
          test_flags=test_flags,
          xs_grad_idxs=[[0, 0]],
          backend_to_test=backend_fw,
          on_device=on_device,
          fn_name=fn_name,
          weights=weights,
          indices=indices,
          max_norm=max_norm,
      )
  
imports: import numpy
  import torch
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 25 for calling a global function with example usage: test_embedding() without return types