[BUG] map_partitions() potentially resulting in unexpected index
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Description
**Describe the bug**
When using map_partitions() on a `dask_cudf` dataframe we can potentially get an unexpected index. This does not affect `.compute()` by default as it reconstructs a global index.
**Steps/Code to reproduce bug**
```
import cudf
import dask_cudf
s = cudf.Series(["ab", "cd", "ef", "gh", "ij"])
ds = dask_cudf.from_cudf(s, 2)
print(ds.compute())
print(ds.map_partitions(lambda x: x.str.character_tokenize(), meta=ds._meta).compute())
```
This prints
```
0 ab
1 cd
2 ef
3 gh
4 ij
dtype: object
0 a
1 b
2 c
3 d
4 e
5 f
0 g
1 h
2 i
3 j
dtype: object
```
**Expected behavior**
Would love to discuss what the expected output would be, but below is what I would expect.
```
0 ab
1 cd
2 ef
3 gh
4 ij
dtype: object
0 a
1 b
2 c
3 d
4 e
5 f
6 g
7 h
8 i
9 j
dtype: object
```
**Environment details**
Click here to see environment details
**git***
commit 512e485c41f0b9aa2261430ba426fa8c05c98c2a (HEAD -> list-accessor, origin/list-accessor)
Merge: bf68778b78 5f9dade58a
Author: Shane Ding
Date: Fri May 7 14:33:32 2021 +0000
Merge branch 'branch-0.20' of https://github.com/rapidsai/cudf into list-accessor
**git submodules***
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***GPU Information***
Mon May 10 17:20:28 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... On | 00000000:06:00.0 Off | 0 |
| N/A 35C P0 48W / 163W | 718MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-SXM2... On | 00000000:07:00.0 Off | 0 |
| N/A 33C P0 42W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-SXM2... On | 00000000:0A:00.0 Off | 0 |
| N/A 34C P0 44W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-SXM2... On | 00000000:0B:00.0 Off | 0 |
| N/A 31C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-SXM2... On | 00000000:85:00.0 Off | 0 |
| N/A 36C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-SXM2... On | 00000000:86:00.0 Off | 0 |
| N/A 35C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-SXM2... On | 00000000:89:00.0 Off | 0 |
| N/A 37C P0 41W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-SXM2... On | 00000000:8A:00.0 Off | 0 |
| N/A 34C P0 42W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 44033 C .../envs/cudf_dev/bin/python 715MiB |
+-----------------------------------------------------------------------------+
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cmake version 3.18.5
CMake suite maintained and supported by Kitware (kitware.com/cmake).
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This is free software; see the source for copying conditions. There is NO
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Cuda compilation tools, release 11.2, V11.2.152
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/raid/sding/miniconda3/envs/cudf_dev/bin/python
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transformers 4.5.1 pyhd8ed1ab_1 conda-forge
typed-ast 1.4.3 py38h497a2fe_0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
unknown 0.0.0 dev_0
urllib3 1.26.4 pyhd8ed1ab_0 conda-forge
virtualenv 20.4.4 py38h578d9bd_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
xz 5.2.5 h516909a_1 conda-forge
yaml 0.2.5 h516909a_0 conda-forge
zeromq 4.3.4 h9c3ff4c_0 conda-forge
zict 2.0.0 py_0 conda-forge
zipp 3.4.1 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h516909a_1010 conda-forge
zstd 1.4.9 ha95c52a_0 conda-forge
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