code: def solve_triangular(
      x1: Union[(None, mx.ndarray.NDArray)],
      x2: Union[(None, mx.ndarray.NDArray)],
      /,
      *,
      upper: bool = True,
      adjoint: bool = False,
      unit_diagonal: bool = False,
      out: Optional[Union[(None, mx.ndarray.NDArray)]] = None,
  ) -> Union[(None, mx.ndarray.NDArray)]:
      # Multiplying with a mask matrix can stop gradients on the diagonal.
      if unit_diagonal:
          w = mx.eye(
              x1.shape[-2], batch_shape=x1.shape[:-2], dtype=x1.dtype
          )
          x1 = w + (1 - w) * x1
      # MXNet does not support complex tensors for this operation,
      # so adjoint always equals transpose.
      return mx.nd.linalg.trsm(
          x1, x2, lower=not upper, transpose=adjoint
      )
  
file_path: ivy/ivy/functional/backends/mxnet/experimental/linear_algebra.py
imports: from typing import Union, Optional, Tuple, List, Sequence
  import mxnet
  from ivy.utils.exceptions import IvyNotImplementedException
label_desc: Use image node_id 8 for calling a global function with example usage: solve_triangular() and returns: mx