code: def tile(
      x: Union[tf.Tensor, tf.Variable],
      /,
      repeats: Sequence[int],
      *,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if x.shape == ():
          x = tf.reshape(x, (-1,))
      if isinstance(repeats, Number):
          repeats = [repeats]
      if isinstance(repeats, tf.Tensor) and repeats.shape == ():
          repeats = tf.reshape(repeats, (-1,))
      # code to unify behaviour with numpy and torch
      if len(x.shape) < len(repeats):
          while len(x.shape) != len(repeats):
              x = tf.expand_dims(x, 0)
      elif len(x.shape) > len(repeats):
          repeats = list(repeats)
          while len(x.shape) != len(repeats):
              repeats = [1] + repeats
      # TODO remove the unifying behaviour code if tensorflow handles this
      # https://github.com/tensorflow/tensorflow/issues/58002
      return tf.tile(x, repeats)
  
imports: import math
  from numbers import Number
  from typing import Union, Tuple, Optional, List, Sequence
  import numpy
  import tensorflow
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.manipulation import _calculate_out_shape
  from .None import backend_version
label_desc: Use image node_id 12 for calling a global function with example usage: tile(repeats) and returns: tf