code: def tile(cls, op):
      a, b, a_axes, b_axes = op.a, op.b, op.a_axes, op.b_axes
  
      c = itertools.count(max(a.ndim, b.ndim))
      a_ax = tuple(
          a_axes.index(i) if i in a_axes else next(c)
          for i in range(a.ndim)
      )
      b_ax = tuple(
          b_axes.index(i) if i in b_axes else next(c)
          for i in range(b.ndim)
      )
      if has_unknown_shape(*op.inputs):
          yield
      a, b = yield from unify_chunks((a, a_ax), (b, b_ax))
      out = op.outputs[0]
  
      a_output_indexes = [
          range(len(a.nsplits[i]))
          for i in range(a.ndim)
          if i not in a_axes
      ]
      b_output_indexes = [
          range(len(b.nsplits[i]))
          for i in range(b.ndim)
          if i not in b_axes
      ]
      output_axes = [
          (0, i) for i in range(a.ndim) if i not in a_axes
      ] + [(1, i) for i in range(b.ndim) if i not in b_axes]
  
      out_chunks = []
      for out_idx in itertools.product(
          *itertools.chain(a_output_indexes, b_output_indexes)
      ):
          a_indexes = [None] * a.ndim
          b_indexes = [None] * b.ndim
          tensor_shape = []
          for i, idx in enumerate(out_idx):
              t_idx, axis = output_axes[i]
              t = (a, b)[t_idx]
              (a_indexes if t_idx == 0 else b_indexes)[axis] = idx
              tensor_shape.append(t.nsplits[axis][idx])
          tensor_shape = tuple(tensor_shape)
  
          tensordot_chunks = []
          for contract_indexes in itertools.product(
              *[range(len(a.nsplits[ax])) for ax in a_axes]
          ):
              a_indices, b_indices = list(a_indexes), list(b_indexes)
              for a_axis, contract_index in zip(
                  a_axes, contract_indexes
              ):
                  a_indices[a_axis] = contract_index
              for b_axis, contract_index in zip(
                  b_axes, contract_indexes
              ):
                  b_indices[b_axis] = contract_index
  
              tensordot_chunk_op = op.copy().reset_key()
              tensordot_chunk = tensordot_chunk_op.new_chunk(
                  [a.cix[tuple(a_indices)], b.cix[tuple(b_indices)]],
                  shape=tensor_shape,
                  order=out.order,
              )
              tensordot_chunks.append(tensordot_chunk)
  
          if len(tensordot_chunks) == 1:
              c = tensordot_chunks[0]
              chunk_op = c.op.copy()
              chunk = chunk_op.new_chunk(
                  c.inputs,
                  shape=c.shape,
                  index=out_idx,
                  order=out.order,
              )
          else:
              chunk = chunk_tree_add(
                  op.dtype,
                  tensordot_chunks,
                  out_idx,
                  tensor_shape,
                  sparse=op.sparse,
              )
          out_chunks.append(chunk)
  
      get_nsplits = lambda t_idx, i: (a, b)[t_idx].nsplits[i]
      nsplits = [get_nsplits(*it) for it in output_axes]
      new_op = op.copy()
      return new_op.new_tensors(
          [a, b], out.shape, chunks=out_chunks, nsplits=nsplits
      )
  
imports: import itertools
  from collections.abc import Iterable
  import numpy
  from ...None import opcodes
  from ...serialization.serializables import FieldTypes, KeyField, TupleField
  from ...utils import has_unknown_shape
  from ..utils import unify_chunks
  from ..array_utils import as_same_device, device, is_sparse_module
  from ..operands import TensorOperand, TensorOperandMixin
  from ..arithmetic.utils import chunk_tree_add
  from ..datasource import tensor
  from ..core import TensorOrder
label_desc: Use image node_id 9 for calling the TensorTensorDot obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op