code: def solve(
      x1: Union[tf.Tensor, tf.Variable],
      x2: Union[tf.Tensor, tf.Variable],
      /,
      *,
      adjoint: bool = False,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if adjoint:
          x1 = tf.linalg.adjoint(x1)
      x1, x2 = ivy.promote_types_of_inputs(x1, x2)
      expanded_last = False
      if len(x2.shape) <= 1:
          if x2.shape[-1] == x1.shape[-1]:
              expanded_last = True
              x2 = tf.expand_dims(x2, axis=1)
      output_shape = tuple(
          tf.broadcast_static_shape(x1.shape[:-2], x2.shape[:-2])
      )
  
      # in case any of the input arrays are empty
      is_empty_x1 = tf.equal(tf.size(x1), 0)
      is_empty_x2 = tf.equal(tf.size(x2), 0)
      if is_empty_x1 or is_empty_x2:
          for i in range(len(x1.shape) - 2):
              x2 = tf.expand_dims(x2, axis=0)
          output_shape = list(output_shape)
          output_shape.append(x2.shape[-2])
          output_shape.append(x2.shape[-1])
          ret = tf.constant([])
          ret = tf.reshape(ret, output_shape)
      else:
          x1 = tf.broadcast_to(x1, output_shape + x1.shape[-2:])
          x2 = tf.broadcast_to(x2, output_shape + x2.shape[-2:])
          ret = tf.linalg.solve(x1, x2)
  
      if expanded_last:
          ret = tf.squeeze(ret, axis=-1)
      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 19 for calling a global function with example usage: solve() and returns: ret