code: def trans_x_to_s(
      x: Union[tf.Tensor, tf.Variable],
      s: Optional[Sequence[int]] = None,
      dim: Sequence[int] = (-2, -1),
  ) -> Union[tf.Tensor, tf.Variable]:
      """Change the shape of the input array x to the desired output shape s."""
      if x.dtype not in [tf.complex128, tf.complex64]:
          x = tf.cast(x, tf.float32)
      x_shape = x.shape
      if dim in [(-1, -2), (1, 0)]:
          s = (s[1], s[0])
      if s[0] >= x_shape[0] and s[1] >= x_shape[1]:
          paddings = tf.constant(
              [[0, s[0] - x_shape[0]], [0, s[1] - x_shape[1]]]
          )
          x_new = tf.pad(x, paddings=paddings)
      elif (s[0] <= x_shape[0] or s[1] <= x_shape[1]) and min(s) > min(
          x_shape
      ):
          x_new = x[: s[0], : s[1]]
          if s[0] != x_new.shape[0]:
              size = s[0] - x_new.shape[0]
              z = tf.zeros((size, s[1]), dtype=x.dtype)
              x_new = tf.concat([x_new, z], 0)
          elif s[1] != x_new.shape[1]:
              size = s[1] - x_new.shape[1]
              z = tf.zeros((s[0], size), dtype=x.dtype)
              x_new = tf.concat([x_new, z], 1)
      elif (s[0] >= x_shape[0] and s[1] <= x_shape[1]) and min(
          s
      ) <= min(x_shape):
          x_new = x[: s[0], : s[1]]
          size = s[0] - x_new.shape[0]
          z = tf.zeros((size, s[1]), dtype=x.dtype)
          x_new = tf.concat([x_new, z], 0)
      elif (s[0] < x_shape[0] and s[1] > x_shape[1]) and min(s) == min(
          x_shape
      ):
          x_new = x[: s[0], : s[1]]
          size = s[1] - x_new.shape[1]
          z = tf.zeros((s[0], size), dtype=x.dtype)
          x_new = tf.concat([x_new, z], axis=1)
      else:
          x_new = x[: s[0], : s[1]]
      return x_new
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 23 for calling a global function with example usage: trans_x_to_s(x, s, dim) and returns: x_new