code: def test_torch_hinge_embedding_loss(
      *,
      dtype_and_x,
      margin,
      size_average,
      reduce,
      reduction,
      test_flags,
      fn_tree,
      backend_fw,
      frontend,
      on_device,
  ):
      input_dtype, x = dtype_and_x
      input, target = x
  
      helpers.test_frontend_function(
          input_dtypes=input_dtype,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          input=input,
          target=target,
          margin=margin,
          size_average=size_average,
          reduce=reduce,
          reduction=reduction,
          atol=1e-5,
          rtol=1e-5,
      )
  
imports: import numpy
  from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  import ivy
  from ivy.functional.frontends.torch.nn.functional.loss_functions import cosine_similarity
label_desc: Use image node_id 6 for calling a global function with example usage: test_torch_hinge_embedding_loss() without return types