code: def test_torch_searchsorted(
      dtype_x_v,
      side,
      out_int32,
      right,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
  ):
      input_dtypes, xs = dtype_x_v
      use_sorter = st.booleans()
      if use_sorter:
          sorter = np.argsort(xs[0])
          sorter = np.array(sorter, dtype=np.int64)
      else:
          xs[0] = np.sort(xs[0])
          sorter = None
      helpers.test_frontend_function(
          input_dtypes=input_dtypes + ["int64"],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          sorted_sequence=xs[0],
          values=xs[1],
          side=side,
          out_int32=out_int32,
          right=right,
          sorter=sorter,
      )
  
imports: import math
  import numpy
  from hypothesis import assume, strategies
  import hypothesis.extra.numpy
  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_linalg import _get_dtype_value1_value2_axis_for_tensordot
label_desc: Use image node_id 41 for calling a global function with example usage: test_torch_searchsorted(dtype_x_v, side, out_int32, right, frontend, test_flags, fn_tree, backend_fw, on_device) without return types