code: def corrcoef(
      x: tf.Tensor,
      /,
      *,
      y: tf.Tensor,
      rowvar: bool = True,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> tf.Tensor:
      if y is None:
          xarr = x
      else:
          axis = 0 if rowvar else 1
          xarr = tf.concat([x, y], axis=axis)
  
      if rowvar:
          mean_t = tf.reduce_mean(xarr, axis=1, keepdims=True)
          cov_t = ((xarr - mean_t) @ tf.transpose(xarr - mean_t)) / (
              x.shape[1] - 1
          )
      else:
          mean_t = tf.reduce_mean(xarr, axis=0, keepdims=True)
          cov_t = (tf.transpose(xarr - mean_t) @ (xarr - mean_t)) / (
              x.shape[1] - 1
          )
  
      cov2_t = tf.linalg.diag(1 / tf.sqrt(tf.linalg.diag_part(cov_t)))
      cor = cov2_t @ cov_t @ cov2_t
      return cor
  
imports: from typing import Union, Optional, Tuple, Sequence
  import tensorflow
  from tensorflow.python.ops.numpy_ops import np_math_ops
  import ivy
  from ivy import with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  from copy import deepcopy
label_desc: Use image node_id 12 for calling a global function with example usage: corrcoef() and returns: cor