code: def _execute_reduce(cls, ctx, op: "TensorReshape"):
      chunk = op.outputs[0]
      try:
          result_array = ctx[chunk.key]
      except KeyError:
          result_array = np.zeros(
              chunk.shape, dtype=chunk.dtype, order=chunk.order.value
          )
      for data_tuple in op.iter_mapper_data(ctx, skip_none=True):
          if data_tuple is None:
              # skip missing partition data
              continue
          result_array[data_tuple[:-1]] = data_tuple[-1]
      ctx[chunk.key] = result_array
  
imports: import itertools
  import logging
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import FieldTypes, KeyField, TupleField, StringField
  from ...utils import has_unknown_shape
  from ..array_utils import as_same_device, device
  from ..datasource import tensor
  from ..operands import TensorOperandMixin, TensorMapReduceOperand, TensorShuffleProxy
  from ..utils import get_order, decide_chunk_sizes
label_desc: Use image node_id 19 for calling the TensorReshape obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types