code: def test_torch_norm(
      *,
      args,
      keepdim,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      p, x_dtype, x, axis, dtype = args
      helpers.test_frontend_function(
          input_dtypes=[x_dtype],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          rtol=1e-01,
          atol=1e-08,
          input=x,
          ord=p,
          dim=axis,
          keepdim=keepdim,
          dtype=dtype,
      )
  
imports: import math
  import sys
  import numpy
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import assert_all_close
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_frontends.test_torch.test_miscellaneous_ops import dtype_value1_value2_axis
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.general_helpers import matrix_is_stable
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _matrix_rank_helper
label_desc: Use image node_id 26 for calling a global function with example usage: test_torch_norm() without return types