code: def test_torch_unique(
      *,
      dtype_x_axis,
      return_inverse,
      return_counts,
      sorted,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      input_dtypes, x, axis = dtype_x_axis
  
      helpers.test_frontend_function(
          input_dtypes=input_dtypes,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          input=x[0],
          sorted=sorted,
          return_inverse=return_inverse,
          return_counts=return_counts,
          dim=axis,
      )
  
imports: from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_statistical import _statistical_dtype_values, _get_castable_dtype
  from ivy_tests.test_ivy.test_functional.test_experimental.test_core.test_statistical import _quantile_helper
label_desc: Use image node_id 26 for calling a global function with example usage: test_torch_unique() without return types