code: def __init__(self, chunk_dim=0, device_count=0):
      self.dim = chunk_dim
      self.devices = [f"rank:{i}/cuda:{i}" for i in range(device_count)]
  
imports: import sys
  import torch
  import torch.nn
  import torch.distributed
  from torch.testing._internal.common_distributed import requires_nccl, skip_if_lt_x_gpu
  from torch.distributed._shard import shard_module
  from torch.distributed._shard.sharding_plan import ShardingPlan, ShardingPlanner
  from torch.distributed._shard.sharding_spec import ChunkShardingSpec
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torch.testing._internal.common_utils import TEST_WITH_DEV_DBG_ASAN, run_tests
  from torch.testing._internal.distributed._shard.sharded_tensor import TEST_GPU_NUM, ShardedTensorTestBase, with_comms
  from torch.testing._internal.distributed._shard.sharded_tensor._test_ops_common import generate_chunk_sharding_specs_for_test
  from torch.testing._internal.distributed._shard.test_common import SimpleMegatronLM
label_desc: Use image node_id 1 to create a new ChunkAllShardingPlanner object from inherited base classes: ShardingPlanner with example: obj = ChunkAllShardingPlanner(chunk_dim, device_count)