code: def test_torch_smooth_l1_loss(
      *,
      dtype_and_x,
      size_average,
      reduce,
      reduction,
      beta,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
  ):
      input_dtype, x = dtype_and_x
      pred_dtype, pred = input_dtype[0], x[0]
      true_dtype, true = input_dtype[1], x[1]
      helpers.test_frontend_function(
          input_dtypes=[pred_dtype, true_dtype],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          input=pred,
          target=true,
          size_average=size_average,
          reduce=reduce,
          reduction=reduction,
          beta=beta,
      )
  
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 16 for calling a global function with example usage: test_torch_smooth_l1_loss() without return types