code: def test_kjt_split(self) -> None:
      class M(torch.nn.Module):
          def forward(
              self, kjt: KeyedJaggedTensor, segments: List[int]
          ):
              return kjt.split(segments)
  
      kjt: KeyedJaggedTensor = make_kjt([2, 3, 4, 5, 6], [1, 2, 1, 1])
      segments: List[int] = [1, 2, 1]
      self._test_kjt_input_module(
          M(),
          kjt.keys(),
          (kjt._values, kjt._lengths, segments),
          test_aot_inductor=False,
      )
  
imports: import sys
  import unittest
  from typing import List, Tuple
  import torch
  import torch._dynamo.skipfiles
  from fbgemm_gpu import sparse_ops
  from torch._export import dynamic_dim
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.shard import _shard_modules
  from torchrec.distributed.test_utils.infer_utils import assert_close, create_test_model_ebc_only, dynamo_skipfiles_allow, KJTInputExportWrapper, prep_inputs, replace_registered_tbes_with_mock_tbes, replace_sharded_quant_modules_tbes_with_mock_tbes, TestQuantEBCSharder
  from torchrec.distributed.types import ShardingEnv, ShardingType
  from torchrec.sparse.jagged_tensor import ComputeKJTToJTDict, KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the TestPt2 obj's underlying member method code with example usage: obj.test_kjt_split() and returns: kjt