code: def solve(
      x1: JaxArray,
      x2: JaxArray,
      /,
      *,
      adjoint: bool = False,
      out: Optional[JaxArray] = None,
  ) -> JaxArray:
      if adjoint:
          x1 = jnp.swapaxes(jnp.conjugate(x1), -1, -2)
      expanded_last = False
      x1, x2 = ivy.promote_types_of_inputs(x1, x2)
      if len(x2.shape) <= 1:
          if x2.shape[-1] == x1.shape[-1]:
              expanded_last = True
              x2 = jnp.expand_dims(x2, axis=1)
  
      # if any of the arrays are empty
      is_empty_x1 = x1.size == 0
      is_empty_x2 = x2.size == 0
      if is_empty_x1 or is_empty_x2:
          for i in range(len(x1.shape) - 2):
              x2 = jnp.expand_dims(x2, axis=0)
          output_shape = list(
              jnp.broadcast_shapes(x1.shape[:-2], x2.shape[:-2])
          )
          output_shape.append(x2.shape[-2])
          output_shape.append(x2.shape[-1])
          ret = jnp.array([]).reshape(output_shape)
      else:
          output_shape = tuple(
              jnp.broadcast_shapes(x1.shape[:-2], x2.shape[:-2])
          )
          x1 = jnp.broadcast_to(x1, output_shape + x1.shape[-2:])
          x2 = jnp.broadcast_to(x2, output_shape + x2.shape[-2:])
          ret = jnp.linalg.solve(x1, x2)
  
      if expanded_last:
          ret = jnp.squeeze(ret, axis=-1)
      return jnp.asarray(ret, dtype=x1.dtype)
  
imports: from collections import namedtuple
  from typing import Union, Optional, Tuple, Literal, Sequence, NamedTuple
  import jax.numpy
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.backends.jax import JaxArray
  from .None import backend_version
  from ivy import promote_types_of_inputs
label_desc: Use image node_id 20 for calling a global function with example usage: solve() and returns: jnp