code: def cov(
      a,
      y=None,
      rowvar=True,
      bias=False,
      ddof=None,
      fweights=None,
      aweights=None,
      *,
      dtype=None,
  ):
      """Returns the covariance matrix of an array.
  
      This function currently does not support ``fweights`` and ``aweights``
      options.
  
      Args:
          a (cupy.ndarray): Array to compute covariance matrix.
          y (cupy.ndarray): An additional set of variables and observations.
          rowvar (bool): If ``True``, then each row represents a variable, with
              observations in the columns. Otherwise, the relationship is
              transposed.
          bias (bool): If ``False``, normalization is by ``(N - 1)``, where N is
              the number of observations given (unbiased estimate). If ``True``,
              then normalization is by ``N``.
          ddof (int): If not ``None`` the default value implied by bias is
              overridden. Note that ``ddof=1`` will return the unbiased estimate
              and ``ddof=0`` will return the simple average.
  
          fweights (cupy.ndarray, int): 1-D array of integer frequency weights.
              the number of times each observation vector should be repeated.
              It is required that fweights >= 0. However, the function will not
              error when fweights < 0 for performance reasons.
          aweights (cupy.ndarray): 1-D array of observation vector weights.
              These relative weights are typically large for observations
              considered "important" and smaller for observations considered
              less "important". If ``ddof=0`` the array of weights can be used
              to assign probabilities to observation vectors.
              It is required that aweights >= 0. However, the function will not
              error when aweights < 0 for performance reasons.
          dtype: Data type specifier. By default, the return data-type will have
              at least `numpy.float64` precision.
  
      Returns:
          cupy.ndarray: The covariance matrix of the input array.
  
      .. seealso:: :func:`numpy.cov`
  
      """
      if ddof is not None and ddof != int(ddof):
          raise ValueError("ddof must be integer")
  
      if a.ndim > 2:
          raise ValueError("Input must be <= 2-d")
  
      if dtype is None:
          if y is None:
              dtype = numpy.promote_types(a.dtype, numpy.float64)
          else:
              if y.ndim > 2:
                  raise ValueError("y must be <= 2-d")
              dtype = functools.reduce(
                  numpy.promote_types, (a.dtype, y.dtype, numpy.float64)
              )
  
      X = cupy.array(a, ndmin=2, dtype=dtype)
      if not rowvar and X.shape[0] != 1:
          X = X.T
      if X.shape[0] == 0:
          return cupy.array([]).reshape(0, 0)
      if y is not None:
          y = cupy.array(y, copy=False, ndmin=2, dtype=dtype)
          if not rowvar and y.shape[0] != 1:
              y = y.T
          X = _core.concatenate_method((X, y), axis=0)
  
      if ddof is None:
          ddof = 0 if bias else 1
  
      w = None
      if fweights is not None:
          if not isinstance(fweights, cupy.ndarray):
              raise TypeError("fweights must be a cupy.ndarray")
          if fweights.dtype.char not in "bBhHiIlLqQ":
              raise TypeError("fweights must be integer")
          fweights = fweights.astype(dtype=float)
          if fweights.ndim > 1:
              raise RuntimeError(
                  "cannot handle multidimensional fweights"
              )
          if fweights.shape[0] != X.shape[1]:
              raise RuntimeError(
                  "incompatible numbers of samples and fweights"
              )
          w = fweights
  
      if aweights is not None:
          if not isinstance(aweights, cupy.ndarray):
              raise TypeError("aweights must be a cupy.ndarray")
          aweights = aweights.astype(dtype=float)
          if aweights.ndim > 1:
              raise RuntimeError(
                  "cannot handle multidimensional aweights"
              )
          if aweights.shape[0] != X.shape[1]:
              raise RuntimeError(
                  "incompatible numbers of samples and aweights"
              )
          if w is None:
              w = aweights
          else:
              w *= aweights
  
      avg, w_sum = cupy.average(X, axis=1, weights=w, returned=True)
      w_sum = w_sum[0]
  
      # Determine the normalization
      if w is None:
          fact = X.shape[1] - ddof
      elif ddof == 0:
          fact = w_sum
      elif aweights is None:
          fact = w_sum - ddof
      else:
          fact = w_sum - ddof * sum(w * aweights) / w_sum
  
      if fact <= 0:
          warnings.warn(
              "Degrees of freedom <= 0 for slice",
              RuntimeWarning,
              stacklevel=2,
          )
          fact = 0.0
  
      X -= X.mean(axis=1)[:, None]
      if w is None:
          X_T = X.T
      else:
          X_T = (X * w).T
      out = X.dot(X_T.conj()) * (1 / cupy.float64(fact))
  
      return out.squeeze()
  
imports: import functools
  import warnings
  import numpy
  import cupy
  from cupy import _core
label_desc: Use image node_id 3 for calling a global function with example usage: cov(a, y, rowvar, bias, ddof, fweights, aweights) and returns: out, cupy