code: def _tile_fancy_index(cls, op: "TensorIndexSetValue"):
      from ..utils import unify_chunks
  
      tensor = op.outputs[0]
      inp = op.inputs[0]
      value = op.value
      indexes = op.indexes
  
      if has_unknown_shape(inp):
          yield
  
      fancy_indexes = [
          index for index in indexes if isinstance(index, ENTITY_TYPE)
      ]
      shape = broadcast_shape(*[ind.shape for ind in fancy_indexes])
      fancy_indexes = [
          broadcast_to(ind, shape) for ind in fancy_indexes
      ]
      if isinstance(value, ENTITY_TYPE):
          value = broadcast_to(value, shape)
          value, *fancy_indexes = yield from unify_chunks(
              value, *fancy_indexes
          )
          value = value.chunks
      else:
          fancy_indexes = yield from unify_chunks(*fancy_indexes)
          value = [value] * len(fancy_indexes[0].chunks)
      input_nsplits = inp.nsplits
      shuffle_axes = tuple(
          axis
          for axis, ind in enumerate(indexes)
          if isinstance(ind, ENTITY_TYPE)
      )
  
      map_chunks = []
      for value_chunk, *index_chunks in zip(
          value, *[index.chunks for index in fancy_indexes]
      ):
          map_op = TensorIndexSetValue(
              stage=OperandStage.map,
              input_nsplits=input_nsplits,
              value=value_chunk,
              indexes=tuple(index_chunks),
              shuffle_axes=shuffle_axes,
              dtype=tensor.dtype,
          )
          inputs = filter_inputs([value_chunk] + list(index_chunks))
          map_chunk = map_op.new_chunk(
              inputs,
              shape=(np.nan,),
              index=index_chunks[0].index,
              order=TensorOrder.C_ORDER,
          )
          map_chunks.append(map_chunk)
  
      proxy_chunk = TensorShuffleProxy(dtype=tensor.dtype).new_chunk(
          map_chunks, shape=(), order=TensorOrder.C_ORDER
      )
  
      reducer_chunks = []
      offsets_on_axis = [
          np.cumsum([0] + list(split)) for split in input_nsplits
      ]
      for input_chunk in inp.chunks:
          chunk_offsets = tuple(
              offsets_on_axis[axis][input_chunk.index[axis]]
              for axis in range(len(inp.shape))
          )
          reducer_op = TensorIndexSetValue(
              stage=OperandStage.reduce,
              n_reducers=len(inp.chunks),
              dtype=input_chunk.dtype,
              shuffle_axes=shuffle_axes,
              chunk_offsets=chunk_offsets,
          )
          reducer_chunk = reducer_op.new_chunk(
              [input_chunk, proxy_chunk],
              index=input_chunk.index,
              shape=input_chunk.shape,
              order=input_chunk.order,
          )
          reducer_chunks.append(reducer_chunk)
  
      new_op = op.copy()
      return new_op.new_tensors(
          op.inputs,
          tensor.shape,
          order=tensor.order,
          chunks=reducer_chunks,
          nsplits=op.input.nsplits,
      )
  
imports: import functools
  import itertools
  import operator
  from numbers import Integral
  from typing import Union
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import Context
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, TupleField, AnyField, BoolField
  from ...tensor import tensor
  from ...utils import has_unknown_shape
  from ..base import broadcast_to
  from ..core import TENSOR_TYPE, TensorOrder
  from ..operands import TensorMapReduceOperand, TensorOperandMixin, TensorShuffleProxy
  from ..utils import broadcast_shape, filter_inputs
  from .core import process_index
label_desc: Use image node_id 6 for calling the TensorIndexSetValue obj's underlying member method code with example usage: obj._tile_fancy_index(cls, op) and returns: new_op