class_docstr: Tests for LAX-backed Numpy reduction operations.
class_docstr_tok: ["Tests","for","LAX-backed","Numpy","reduction","operations","."]
code: def testCov(
      self,
      shape,
      dtype,
      y_shape,
      y_dtype,
      rowvar,
      ddof,
      bias,
      fweights,
      aweights,
  ):
      rng = jtu.rand_default(self.rng())
      wrng = jtu.rand_positive(self.rng())
      wdtype = np.real(dtype(0)).dtype
      wshape = shape[-1:] if rowvar or shape[0] == 1 else shape[:1]
  
      args_maker = lambda: [
          rng(shape, dtype),
          rng(y_shape, y_dtype) if y_dtype else None,
          wrng(wshape, int) if fweights else None,
          wrng(wshape, wdtype) if aweights else None,
      ]
      kwargs = dict(rowvar=rowvar, ddof=ddof, bias=bias)
      np_fun = lambda m, y, f, a: np.cov(
          m, y, fweights=f, aweights=a, **kwargs
      )
      jnp_fun = lambda m, y, f, a: jnp.cov(
          m, y, fweights=f, aweights=a, **kwargs
      )
      tol = {
          jnp.bfloat16: 5e-2,
          np.float16: 1e-2,
          np.float32: 1e-5,
          np.float64: 1e-13,
          np.complex64: 1e-5,
          np.complex128: 1e-13,
      }
      tol = jtu.join_tolerance(tol, jtu.tolerance(dtype))
      self._CheckAgainstNumpy(
          np_fun, jnp_fun, args_maker, check_dtypes=False, tol=tol
      )
      self._CompileAndCheck(jnp_fun, args_maker, atol=tol, rtol=tol)
  
imports: import collections
  from functools import partial
  import itertools
  import unittest
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import jax
  from jax import numpy
  from jax._src import config
  from jax._src import dtypes
  from jax._src import test_util
  from jax._src.util import NumpyComplexWarning
label_desc: Use image node_id 16 for calling the JaxNumpyReducerTests obj's underlying member method code with example usage: obj.testCov(shape, dtype, y_shape, y_dtype, rowvar, ddof, bias, fweights, aweights) without return types