code: def tile(cls, op):
      out_tensor = op.outputs[0]
      input_scripts, output_scripts = op.subscripts.split("->")
      tensor_axes = list(zip(op.inputs, input_scripts.split(",")))
  
      # rechunk to unify nsplits
      input_nsplits = defaultdict(list)
      for t, axes in tensor_axes:
          for splits, ax in zip(t.nsplits, axes):
              input_nsplits[ax].append(splits)
      input_tensors = []
      for t, axes in tensor_axes:
          new_nsplits = tuple(
              decide_unify_split(*input_nsplits[ax])
              if t.shape[j] > 1
              else (t.shape[j],)
              for j, ax in enumerate(axes)
          )
          input_tensors.append(
              (yield from recursive_tile(t.rechunk(new_nsplits)))
          )
  
      tensor_indexes = dict()
      output_axes = defaultdict(list)
      axes_splits = dict()
      tensor_contract_axes = []
      for i, (t, axes) in enumerate(
          zip(input_tensors, input_scripts.split(","))
      ):
          for j in range(t.ndim):
              if axes[j] in output_scripts:
                  # Record the output tensor's axes and its nsplit.
                  tensor_indexes[axes[j]] = range(len(t.nsplits[j]))
                  output_axes[axes[j]].append((i, j))
          axis_splits = dict(
              (axes[j], t.nsplits[j]) for j in range(t.ndim)
          )
          axes_splits.update(axis_splits)
          tensor_contract_axes.append(
              [ax for ax in axes if ax not in output_scripts]
          )
  
      out_chunks = []
      output_indexes = [tensor_indexes[ax] for ax in output_scripts]
      for out_idx in itertools.product(*output_indexes):
          all_indexes = [[None] * t.ndim for t in input_tensors]
          tensor_shape = []
          for i, idx in enumerate(out_idx):
              tensor_shape.append(axes_splits[output_scripts[i]][idx])
              for t_idx, axis in output_axes[output_scripts[i]]:
                  if input_tensors[t_idx].shape[axis] == 1:
                      all_indexes[t_idx][axis] = 0
                  else:
                      all_indexes[t_idx][axis] = idx
          tensor_shape = tuple(tensor_shape)
          einsum_chunks = []
          contract_axes = [
              s
              for s in set(input_scripts.replace(",", ""))
              if s not in output_scripts
          ]
          for contract_indexes in itertools.product(
              *[range(len(axes_splits[ax])) for ax in contract_axes]
          ):
              for j, t_contract_axes in enumerate(tensor_contract_axes):
                  for axis in t_contract_axes:
                      axis_index = tensor_axes[j][1].index(axis)
                      all_indexes[j][axis_index] = contract_indexes[
                          contract_axes.index(axis)
                      ]
              einsum_op = op.copy().reset_key()
              in_chunks = [
                  t.cix[tuple(indices)]
                  for t, indices in zip(input_tensors, all_indexes)
              ]
              chunk = einsum_op.new_chunk(
                  in_chunks, shape=tensor_shape, order=out_tensor.order
              )
              einsum_chunks.append(chunk)
  
          if len(einsum_chunks) == 1:
              c = einsum_chunks[0]
              chunk_op = c.op.copy()
              chunk = chunk_op.new_chunk(
                  c.inputs,
                  shape=c.shape,
                  index=out_idx,
                  order=out_tensor.order,
              )
          else:
              chunk = chunk_tree_add(
                  op.dtype,
                  einsum_chunks,
                  out_idx,
                  tensor_shape,
                  sparse=op.sparse,
              )
          out_chunks.append(chunk)
  
      nsplits = [axes_splits[ax] for ax in output_scripts]
      new_op = op.copy()
      return new_op.new_tensors(
          input_tensors,
          out_tensor.shape,
          chunks=out_chunks,
          nsplits=nsplits,
      )
  
imports: import itertools
  from collections import defaultdict
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import AnyField, StringField
  from ..array_utils import as_same_device, device
  from ..arithmetic.utils import chunk_tree_add
  from ..core import TensorOrder
  from ..operands import TensorOperand, TensorOperandMixin
  from ..utils import decide_unify_split
  from .einsumfunc import parse_einsum_input, einsum_path
label_desc: Use image node_id 7 for calling the TensorEinsum obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op