code: def _tile_one_chunk(cls, op, a, p):
      out = op.outputs[0]
      chunk_op = op.copy().reset_key()
      chunk_op.seed = gen_random_seeds(
          1, np.random.RandomState(op.seed)
      )[0]
      chunk_inputs = []
      if isinstance(a, TENSOR_TYPE):
          chunk_op.a = a.chunks[0]
          chunk_inputs.append(chunk_op.a)
      else:
          chunk_op.a = a
      if isinstance(p, TENSOR_TYPE):
          chunk_op.p = p.chunks[0]
          chunk_inputs.append(chunk_op.p)
      else:
          chunk_op.p = p
      chunk = chunk_op.new_chunk(
          chunk_inputs,
          shape=out.shape,
          index=(0,) * out.ndim,
          order=out.order,
      )
  
      new_op = op.copy()
      return new_op.new_tensors(
          op.inputs,
          shape=out.shape,
          order=out.order,
          chunks=[chunk],
          nsplits=tuple((s,) for s in out.shape),
      )
  
imports: from numbers import Integral
  import numpy
  from ...None import opcodes
  from ...config import options
  from ...core import recursive_tile
  from ...serialization.serializables import FieldTypes, AnyField, KeyField, BoolField, TupleField
  from ...utils import has_unknown_shape, ceildiv
  from ..operands import TensorOperandMixin
  from ..core import TENSOR_TYPE, TENSOR_CHUNK_TYPE, TensorOrder
  from ..datasource import arange, array
  from ..utils import decide_chunk_sizes, normalize_chunk_sizes, gen_random_seeds
  from ..array_utils import as_same_device, device
  from .core import TensorRandomOperand, RandomState
label_desc: Use image node_id 3 for calling the TensorChoice obj's underlying member method code with example usage: obj._tile_one_chunk(cls, op, a, p) and returns: new_op