code: def cholesky(
      x: Union[tf.Tensor, tf.Variable],
      /,
      *,
      upper: bool = False,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if not upper:
          ret = tf.linalg.cholesky(x)
      else:
          axes = list(range(len(x.shape) - 2)) + [
              len(x.shape) - 1,
              len(x.shape) - 2,
          ]
          ret = tf.transpose(
              tf.linalg.cholesky(tf.transpose(x, perm=axes)), perm=axes
          )
      return ret
  
imports: from typing import Union, Optional, Tuple, Literal, List, NamedTuple, Sequence
  from collections import namedtuple
  import tensorflow
  from tensorflow.python.framework.dtypes import DType
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 1 for calling a global function with example usage: cholesky() and returns: ret