code: def test_torch_cov(
      dtype_x1_corr_cov,
      test_flags,
      frontend,
      fn_tree,
      on_device,
      backend_fw,
  ):
      dtype, x1, correction, fweights, aweights = dtype_x1_corr_cov
      helpers.test_frontend_function(
          input_dtypes=["float64", "int64", "float64"],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          rtol=1e-2,
          atol=1e-2,
          input=x1,
          correction=correction,
          fweights=fweights,
          aweights=aweights,
      )
  
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 17 for calling a global function with example usage: test_torch_cov(dtype_x1_corr_cov, test_flags, frontend, fn_tree, on_device, backend_fw) without return types