code: def _tile_sample_with_replacement(cls, op, a, nsplits):
      out_shape = tuple(sum(ns) for ns in nsplits)
      out_size = np.prod(out_shape).item()
      most_chunk_size = np.prod([max(ns) for ns in nsplits]).item()
  
      is_a_int = False
      if isinstance(a, Integral):
          is_a_int = True
          a_size = a
      else:
          a = array(a)
          a_size = a.size
  
      rs = RandomState.from_numpy(np.random.RandomState(op.seed))
  
      if is_a_int:
          # the indices is just the result
          ret = rs.randint(a_size, size=out_shape, chunk_size=nsplits)
      else:
          # gen indices first, need to be flattened
          indices = rs.randint(
              a_size, size=out_size, chunk_size=most_chunk_size
          )
          # get result via fancy indexing
          ret = a[indices]
          if len(out_shape) > 1:
              # reshape back if out's ndim > 1
              ret = ret.reshape(out_shape)
          ret = ret.rechunk(nsplits)
  
      return [(yield from recursive_tile(ret))]
  
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 4 for calling the TensorChoice obj's underlying member method code with example usage: obj._tile_sample_with_replacement(cls, op, a, nsplits) and returns: list