code: def tile(cls, op):
      from ..arithmetic.subtract import TensorSubtract
      from ..arithmetic.utils import chunk_tree_add
      from .dot import TensorDot
  
      if has_unknown_shape(*op.inputs):
          yield
  
      a, b = op.a, op.b
      unified_nsplit = decide_unify_split(
          a.nsplits[0], a.nsplits[1], b.nsplits[0]
      )
      a = yield from recursive_tile(
          a.rechunk((unified_nsplit, unified_nsplit))
      )
      b = yield from recursive_tile(
          b.rechunk((unified_nsplit,) + b.nsplits[1:])
      )
  
      b_multi_dim = b.ndim > 1
      b_hsplits = b.chunk_shape[1] if b_multi_dim else 1
  
      def _x_shape(a_shape, b_shape):
          return (
              (a_shape[1],)
              if len(b_shape) == 1
              else (a_shape[1], b_shape[1])
          )
  
      def _dot_shape(a_shape, b_shape):
          return (
              (a_shape[0],)
              if len(b_shape) == 1
              else (a_shape[0], b_shape[1])
          )
  
      lower = op.lower
      out_chunks = {}
      if lower:
          i_range = range(a.chunk_shape[0])
      else:
          i_range = range(a.chunk_shape[0] - 1, -1, -1)
      for i in i_range:
          target_a = a.cix[i, i]
          for j in range(b_hsplits):
              idx = (i, j) if b_multi_dim else (i,)
              target_b = b.cix[idx]
              if (lower and i > 0) or (
                  not lower and i < a.chunk_shape[0] - 1
              ):
                  prev_chunks = []
                  if lower:
                      k_range = range(i)
                  else:
                      k_range = range(i + 1, a.chunk_shape[0])
                  for k in k_range:
                      a_chunk, b_chunk = (
                          a.cix[i, k],
                          out_chunks[(k, j) if b_multi_dim else (k,)],
                      )
                      prev_chunk = TensorDot(
                          dtype=op.dtype, sparse=a_chunk.issparse()
                      ).new_chunk(
                          [a_chunk, b_chunk],
                          shape=_dot_shape(
                              a_chunk.shape, b_chunk.shape
                          ),
                      )
                      prev_chunks.append(prev_chunk)
                  if len(prev_chunks) == 1:
                      s = prev_chunks[0]
                  else:
                      s = chunk_tree_add(
                          prev_chunks[0].dtype,
                          prev_chunks,
                          None,
                          prev_chunks[0].shape,
                          sparse=op.sparse,
                      )
                  target_b = TensorSubtract(
                      dtype=op.dtype, lhs=target_b, rhs=s
                  ).new_chunk([target_b, s], shape=target_b.shape)
              out_chunk = TensorSolveTriangular(
                  lower=lower, sparse=op.sparse, dtype=op.dtype
              ).new_chunk(
                  [target_a, target_b],
                  shape=_x_shape(target_a.shape, target_b.shape),
                  index=idx,
                  order=op.outputs[0].order,
              )
              out_chunks[out_chunk.index] = out_chunk
  
      new_op = op.copy()
      nsplits = (
          (a.nsplits[0],)
          if b.ndim == 1
          else (a.nsplits[0], b.nsplits[1])
      )
      return new_op.new_tensors(
          op.inputs,
          op.outputs[0].shape,
          chunks=list(out_chunks.values()),
          nsplits=nsplits,
      )
  
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 8 for calling the TensorSolveTriangular obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op, unknown, unknown