code: def execute(cls, ctx, op):
      (a, b), device_id, xp = as_same_device(
          [ctx[c.key] for c in op.inputs],
          device=op.device,
          ret_extra=True,
      )
  
      chunk = op.outputs[0]
      with device(device_id):
          if xp is np:
              import scipy.linalg
  
              try:
                  ctx[chunk.key] = scipy.linalg.solve_triangular(
                      a, b, lower=op.lower
                  )
              except np.linalg.LinAlgError:
                  if op.strict is not False:
                      raise
                  ctx[chunk.key] = np.linalg.lstsq(a, b, rcond=-1)[0]
          elif xp is cp:
              import cupyx
  
              ctx[chunk.key] = cupyx.scipy.linalg.solve_triangular(
                  a, b, lower=op.lower
              )
          else:
              ctx[chunk.key] = xp.solve_triangular(
                  a, b, lower=op.lower, sparse=op.sparse
              )
  
imports: import numpy
  from numpy.linalg import LinAlgError
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import BoolField, KeyField
  from ...utils import has_unknown_shape
  from ..array_utils import device, as_same_device, cp
  from ..operands import TensorOperand, TensorOperandMixin
  from ..datasource import tensor
  from ..utils import decide_unify_split
  from ..core import TensorOrder
label_desc: Use image node_id 9 for calling the TensorSolveTriangular obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types