code: def test_numpy_var(
      dtype_and_x,
      dtype,
      where,
      frontend,
      backend_fw,
      test_flags,
      fn_tree,
      on_device,
      keep_dims,
  ):
      input_dtypes, x, axis, correction = dtype_and_x
      if isinstance(axis, tuple):
          axis = axis[0]
      (
          where,
          input_dtypes,
          test_flags,
      ) = np_frontend_helpers.handle_where_and_array_bools(
          where=where,
          input_dtype=input_dtypes,
          test_flags=test_flags,
      )
      assume(np.dtype(dtype[0]) >= np.dtype(input_dtypes[0]))
      np_frontend_helpers.test_frontend_function(
          input_dtypes=input_dtypes,
          frontend=frontend,
          backend_to_test=backend_fw,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          rtol=1e-1,
          atol=1e-1,
          x=x[0],
          axis=axis,
          ddof=correction,
          keepdims=keep_dims,
          out=None,
          dtype=dtype[0],
          where=where,
      )
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_numpy/test_statistics/test_averages_and_variances.py
imports: from hypothesis import strategies, assume
  import numpy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.test_functional.test_core.test_statistical import _statistical_dtype_values
  import ivy_tests.test_ivy.test_frontends.test_numpy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 10 for calling a global function with example usage: test_numpy_var(dtype_and_x, dtype, where, frontend, backend_fw, test_flags, fn_tree, on_device, keep_dims) without return types