code: def corrcoef(
      a, y=None, rowvar=True, bias=None, ddof=None, *, dtype=None
  ):
      """Returns the Pearson product-moment correlation coefficients of an array.
  
      Args:
          a (cupy.ndarray): Array to compute the Pearson product-moment
              correlation coefficients.
          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 (None): Has no effect, do not use.
          ddof (None): Has no effect, do not use.
          dtype: Data type specifier. By default, the return data-type will have
              at least `numpy.float64` precision.
  
      Returns:
          cupy.ndarray: The Pearson product-moment correlation coefficients of
          the input array.
  
      .. seealso:: :func:`numpy.corrcoef`
  
      """
      if bias is not None or ddof is not None:
          warnings.warn(
              "bias and ddof have no effect and are deprecated",
              DeprecationWarning,
          )
  
      out = cov(a, y, rowvar, dtype=dtype)
      try:
          d = cupy.diag(out)
      except ValueError:
          return out / out
  
      stddev = cupy.sqrt(d.real)
      out /= stddev[:, None]
      out /= stddev[None, :]
  
      cupy.clip(out.real, -1, 1, out=out.real)
      if cupy.iscomplexobj(out):
          cupy.clip(out.imag, -1, 1, out=out.imag)
  
      return out
  
imports: import functools
  import warnings
  import numpy
  import cupy
  from cupy import _core
label_desc: Use image node_id 1 for calling a global function with example usage: corrcoef(a, y, rowvar, bias, ddof) and returns: out, unknown