code: def solve(
      x1: torch.Tensor,
      x2: torch.Tensor,
      /,
      *,
      adjoint: bool = False,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if adjoint:
          x1 = torch.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 = torch.unsqueeze(x2, dim=1)
  
      is_empty_x1 = x1.nelement() == 0
      is_empty_x2 = x2.nelement() == 0
      if is_empty_x1 or is_empty_x2:
          for i in range(len(x1.shape) - 2):
              x2 = torch.unsqueeze(x2, dim=0)
          output_shape = list(
              torch.broadcast_shapes(x1.shape[:-2], x2.shape[:-2])
          )
          output_shape.append(x2.shape[-2])
          output_shape.append(x2.shape[-1])
          ret = torch.Tensor([])
          ret = torch.reshape(ret, output_shape)
      else:
          ret = torch.linalg.solve(x1, x2)
  
      if expanded_last:
          ret = torch.squeeze(ret, dim=-1)
      return ret
  
imports: import torch
  from typing import Union, Optional, Tuple, Literal, List, NamedTuple, Sequence
  from collections import namedtuple
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from .elementwise import _cast_for_unary_op
label_desc: Use image node_id 20 for calling a global function with example usage: solve() and returns: ret