code: def solve(
      x1: Union[ivy.Array, ivy.NativeArray],
      x2: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      adjoint: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Return the solution x to the system of linear equations represented by
      the well- determined (i.e., full rank) linear matrix equation Ax = B.
  
      Parameters
      ----------
      x1
          coefficient array A having shape (..., M, M) and whose innermost two dimensions
          form square matrices. Must be of full rank (i.e., all rows or, equivalently,
          columns must be linearly independent). Should have a floating-point data type.
      x2
          ordinate (or “dependent variable”) array B. If x2 has shape (M,1), x2 is
          equivalent to an array having shape (..., M, 1). If x2 has shape (..., M, K),
          each column k defines a set of ordinate values for which to compute a solution,
          and shape(x2)[:-1] must be compatible with shape(x1)[:-1] (see Broadcasting).
          Should have a floating-point data type.
      adjoint
          specifies whether the system should be solved for x1 or adjoint(x1)
      out
          optional output array, for writing the result to. It must have a shape that the
          inputs broadcast to.
  
      Returns
      -------
      ret
          an array containing the solution to the system AX = B (or adjoint(A)X = B)
          for each square matrix. The returned array must have the same shape as x2
          (i.e., the array corresponding to B) and must have a floating-point data
          type determined by Type Promotion Rules.
  
      This function conforms to the `Array API Standard
      <https://data-apis.org/array-api/latest/>`_. This docstring is an extension of the
      `docstring <https://data-apis.org/array-api/latest/
      extensions/generated/array_api.linalg.solve.html>`_
      in the standard.
  
      Both the description and the type hints above assume an array input for simplicity,
      but this function is *nestable*, and therefore also accepts :class:`ivy.Container`
      instances in place of any of the arguments.
  
      Examples
      --------
      With class:`ivy.Array` input:
      >>> A = ivy.array([[1.1, 1.2, 1.3],
                         [2.1, 2.2, 2.3],
                         [3.1, 3.2, 3.3]]),
      >>> B = ivy.array([[1.1],
                         [2.1],
                         [3.1]]),
      >>> x = solve(A,B);
      >>> print(x)
      ivy.array([[1],
                 [0],
                 [0]])
      >>> print(x.shape)
      (1,3)
  
      With shape(A) = (2,3,3) and shape(B) = (2,3,1):
      >>> A = ivy.array([[[11.1, 11.2, 11.3],
                          [12.1, 12.2, 12.3],
                          [13.1, 13.2, 13.3]],
                         [[21.1, 21.2, 21.3],
                          [22.1, 22.2, 22.3],
                          [23.1, 23.2, 23.3]]
                          ]),
      >>> B = ivy.array([[[11.1],
                          [12.1],
                          [13.1]],
                         [[21.1],
                          [22.1],
                          [23.1]]]),
      >>> x = solve(A,B);
      >>> print(x)
      ivy.array([[[1],
                  [0],
                  [0]],
                 [[1],
                  [0],
                  [0]]])
      >>> print(x.shape)
      (2,1,3)
  
      With shape(A) = (3,3) and shape(B) = (3,2):
      >>> A = ivy.array([[1.1, 1.2, 1.3],
                         [2.1, 2.2, 2.3],
                         [3.1, 3.2, 3.3]]),
      >>> B = ivy.array([[1.1, 2.2],
                         [2.1, 4.2],
                         [3.1, 6.2]]),
      >>> x = solve(A,B);
      >>> print(x)
      ivy.array([[[1],
                  [0],
                  [0]],
                 [[2],
                  [0],
                  [0]]])
      >>> print(x.shape)
      (2,1,3)
  
      With class:`ivy.Container` input:
      >>> A = ivy.array([[1.1, 1.2, 1.3],
                         [2.1, 2.2, 2.3],
                         [3.1, 3.2, 3.3]]),
      >>> B = ivy.container(B1 = ivy.array([[1.1], [2.1], [3.1]]),
                            B2 = ivy.array([[2.2], [4.2], [6.2]]))
      >>> x = solve(A,B);
      >>> print(x)
      {
          B1:([[1],[0],[0]]),
          B2:([[2],[0],[0]])
      }
      """
      return current_backend(x1, x2).solve(
          x1, x2, adjoint=adjoint, out=out
      )
  
imports: from typing import Union, Optional, Tuple, Literal, List, Sequence
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import handle_array_function, to_native_arrays_and_back, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 19 for calling a global function with example usage: solve() and returns: current_backend