code: def test_torch_cdist(
      *,
      dtypes_and_x,
      p,
      compute_mode,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      input_dtypes, xs = dtypes_and_x
      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,
          x1=xs[0],
          x2=xs[1],
          p=p,
          compute_mode=compute_mode,
      )
  
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 14 for calling a global function with example usage: test_torch_cdist() without return types