docstr: Return the flattened gradients slices in the bucket, the data orginization of the flattened tensor:
  [grad0_rank0, grad1_rank0, ..., grad_0_rank1, grad1_rank1, ....]
  
  Returns:
      Tensor: the flattened gradients slices in the bucket
docstr_tok: ["Return","the","flattened","gradients","slices","in","the","bucket",",","the","data","orginization","of","the","flattened","tensor",":","[","grad0_rank0",",","grad1_rank0",",","...",",","grad_0_rank1",",","grad1_rank1",",","....","]","Returns",":","Tensor",":","the","flattened","gradients","slices","in","the","bucket"]
code: def get_flatten_grad(self) -> Tensor:
      """Return the flattened gradients slices in the bucket, the data orginization of the flattened tensor:
      [grad0_rank0, grad1_rank0, ..., grad_0_rank1, grad1_rank1, ....]
  
      Returns:
          Tensor: the flattened gradients slices in the bucket
      """
  
      flat_grad = []
      for grad_list in self._grad_in_bucket.values():
          flat_grad.append(_flatten_dense_tensors(grad_list))
      flat_grad = _flatten_dense_tensors(flat_grad)
      return flat_grad
  
file_path: colossalai/colossalai/zero/low_level/bookkeeping/bucket_store.py
imports: from typing import Dict
  import torch
  from torch import Tensor
  from torch._utils import _flatten_dense_tensors
  from torch.distributed import ProcessGroup
  from .base_store import BaseStore
label_desc: Use image node_id 7 for calling the BucketStore obj's underlying member method code with example usage: obj.get_flatten_grad() and returns: flat_grad