code: def unpack(
      t: TensorLike,
      layout: layout_lib.Layout,
      split_fn=np.split,
      stack_fn=np.stack,
  ) -> List[TensorLike]:
      """Slice `t` into a flattened list of tensors suitable for `pack`."""
      if not layout.rank:
          return [t] * layout.mesh.size
      sharded_tensor = _split(
          t,
          [layout.num_shards(i) for i in range(layout.rank)],
          split_fn=split_fn,
          stack_fn=stack_fn,
      )
      flattened = [np.ndarray([])] * layout.mesh.size
      for offset, shard in enumerate(layout.offset_to_shard()):
          flattened[offset] = sharded_tensor[tuple(shard)]
      return flattened
  
file_path: tensorflow/tensorflow/dtensor/python/numpy_util.py
imports: from typing import List
  import numpy
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import layout
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.types.core import Tensor, TensorLike
label_desc: Use image node_id 4 for calling a global function with example usage: unpack(t, layout, split_fn, stack_fn) and returns: flattened, unknown