code: def corrcoef(x, y=None, rowvar=True):
      r"""
      Return Pearson product-moment correlation coefficients.
  
      Please refer to the documentation for `cov` for more detail.  The
      relationship between the correlation coefficient matrix, `R`, and the
      covariance matrix, `C`, is
  
      .. math:: R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }
  
      The values of `R` are between -1 and 1, inclusive.
  
      Parameters
      ----------
      x : array_like
          A 1-D or 2-D array containing multiple variables and observations.
          Each row of `x` represents a variable, and each column a single
          observation of all those variables. Also see `rowvar` below.
      y : array_like, optional
          An additional set of variables and observations. `y` has the same
          shape as `x`.
      rowvar : bool, optional
          If `rowvar` is True (default), then each row represents a
          variable, with observations in the columns. Otherwise, the relationship
          is transposed: each column represents a variable, while the rows
          contain observations.
  
      Returns
      -------
      R : Tensor
          The correlation coefficient matrix of the variables.
  
      See Also
      --------
      cov : Covariance matrix
  
      Notes
      -----
      Due to floating point rounding the resulting tensor may not be Hermitian,
      the diagonal elements may not be 1, and the elements may not satisfy the
      inequality abs(a) <= 1. The real and imaginary parts are clipped to the
      interval [-1,  1] in an attempt to improve on that situation but is not
      much help in the complex case.
  
      This function accepts but discards arguments `bias` and `ddof`.  This is
      for backwards compatibility with previous versions of this function.  These
      arguments had no effect on the return values of the function and can be
      safely ignored in this and previous versions of numpy.
  
      """
      from ..arithmetic import sqrt
      from ..datasource import diag
  
      c = cov(x, y, rowvar)
      if c.ndim == 0:
          return c / c
      d = diag(c)
      d = d.reshape(d.shape[0], 1)
      sqrt_d = sqrt(d)
      return (c / sqrt_d) / sqrt_d.T
  
label_desc: Use image node_id 1 for calling a global function with example usage: corrcoef(x, y, rowvar) and returns: unknown, unknown