We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Pylint: R1710
```bash
mmpose/apis/test.py:142:0: R1710: Either all return statements in a function should return an expression, or none of them should. (inconsistent-return-statements)
mmpose/datasets/datasets/mesh/mesh_mix_dataset.py:38:4: R1710: Either all return statements in a function should return an expression, or none of them should. (inconsistent-return-statements)
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mmpose/datasets/datasets/mesh/mesh_mix_dataset.py`
Content:
```
1 from abc import ABCMeta
2 
3 import numpy as np
4 from torch.utils.data import Dataset
5 
6 from mmpose.datasets.builder import DATASETS
7 from .mesh_base_dataset import MeshBaseDataset
8 
9 
10 @DATASETS.register_module()
11 class MeshMixDataset(Dataset, metaclass=ABCMeta):
12     """Mix Dataset for 3D human mesh estimation.
13 
14     The dataset combines data from multiple datasets (MeshBaseDataset) and
15     sample the data from different datasets with the provided proportions.
16     The dataset loads raw features and apply specified transforms
17     to return a dict containing the image tensors and other information.
18 
19     Args:
20         configs (list): List of configs for multiple datasets.
21         partition (list): Sample proportion of multiple datasets.
22             The the elements of it should be non-negative and the
23             sum of it should be 1.
24     """
25 
26     def __init__(self, configs, partition):
27         """Load data from multiple datasets."""
28         assert min(partition) >= 0
29         assert sum(partition) == 1
30         self.partition = np.array(partition).cumsum()
31         self.datasets = [MeshBaseDataset(**cfg) for cfg in configs]
32         self.length = max(len(ds) for ds in self.datasets)
33 
34     def __len__(self):
35         """Get the size of the dataset."""
36         return self.length
37 
38     def __getitem__(self, idx):
39         """Given index, sample the data from multiple datasets with the given
40         proportion."""
41         p = np.random.rand()
42         for i in range(len(self.datasets)):
43             if p <= self.partition[i]:
44                 index_new = (idx + np.random.rand()) * len(
45                     self.datasets[i]) / self.length
46                 index_new = int(np.round(index_new)) % (len(self.datasets[i]))
47                 return self.datasets[i][index_new]
48
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py b/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py
--- a/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py
+++ b/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py
@@ -45,3 +45,4 @@
                     self.datasets[i]) / self.length
                 index_new = int(np.round(index_new)) % (len(self.datasets[i]))
                 return self.datasets[i][index_new]
+        return None

{"golden_diff": "diff --git a/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py b/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py\n--- a/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py\n+++ b/mmpose/datasets/datasets/mesh/mesh_mix_dataset.py\n@@ -45,3 +45,4 @@\n                     self.datasets[i]) / self.length\n                 index_new = int(np.round(index_new)) % (len(self.datasets[i]))\n                 return self.datasets[i][index_new]\n+        return None\n", "issue": "Pylint: R1710\n```bash\r\nmmpose/apis/test.py:142:0: R1710: Either all return statements in a function should return an expression, or none of them should. (inconsistent-return-statements)\r\nmmpose/datasets/datasets/mesh/mesh_mix_dataset.py:38:4: R1710: Either all return statements in a function should return an expression, or none of them should. (inconsistent-return-statements)\r\n```\n", "before_files": [{"content": "from abc import ABCMeta\n\nimport numpy as np\nfrom torch.utils.data import Dataset\n\nfrom mmpose.datasets.builder import DATASETS\nfrom .mesh_base_dataset import MeshBaseDataset\n\n\n@DATASETS.register_module()\nclass MeshMixDataset(Dataset, metaclass=ABCMeta):\n    \"\"\"Mix Dataset for 3D human mesh estimation.\n\n    The dataset combines data from multiple datasets (MeshBaseDataset) and\n    sample the data from different datasets with the provided proportions.\n    The dataset loads raw features and apply specified transforms\n    to return a dict containing the image tensors and other information.\n\n    Args:\n        configs (list): List of configs for multiple datasets.\n        partition (list): Sample proportion of multiple datasets.\n            The the elements of it should be non-negative and the\n            sum of it should be 1.\n    \"\"\"\n\n    def __init__(self, configs, partition):\n        \"\"\"Load data from multiple datasets.\"\"\"\n        assert min(partition) >= 0\n        assert sum(partition) == 1\n        self.partition = np.array(partition).cumsum()\n        self.datasets = [MeshBaseDataset(**cfg) for cfg in configs]\n        self.length = max(len(ds) for ds in self.datasets)\n\n    def __len__(self):\n        \"\"\"Get the size of the dataset.\"\"\"\n        return self.length\n\n    def __getitem__(self, idx):\n        \"\"\"Given index, sample the data from multiple datasets with the given\n        proportion.\"\"\"\n        p = np.random.rand()\n        for i in range(len(self.datasets)):\n            if p <= self.partition[i]:\n                index_new = (idx + np.random.rand()) * len(\n                    self.datasets[i]) / self.length\n                index_new = int(np.round(index_new)) % (len(self.datasets[i]))\n                return self.datasets[i][index_new]\n", "path": "mmpose/datasets/datasets/mesh/mesh_mix_dataset.py"}], "after_files": [{"content": "from abc import ABCMeta\n\nimport numpy as np\nfrom torch.utils.data import Dataset\n\nfrom mmpose.datasets.builder import DATASETS\nfrom .mesh_base_dataset import MeshBaseDataset\n\n\n@DATASETS.register_module()\nclass MeshMixDataset(Dataset, metaclass=ABCMeta):\n    \"\"\"Mix Dataset for 3D human mesh estimation.\n\n    The dataset combines data from multiple datasets (MeshBaseDataset) and\n    sample the data from different datasets with the provided proportions.\n    The dataset loads raw features and apply specified transforms\n    to return a dict containing the image tensors and other information.\n\n    Args:\n        configs (list): List of configs for multiple datasets.\n        partition (list): Sample proportion of multiple datasets.\n            The the elements of it should be non-negative and the\n            sum of it should be 1.\n    \"\"\"\n\n    def __init__(self, configs, partition):\n        \"\"\"Load data from multiple datasets.\"\"\"\n        assert min(partition) >= 0\n        assert sum(partition) == 1\n        self.partition = np.array(partition).cumsum()\n        self.datasets = [MeshBaseDataset(**cfg) for cfg in configs]\n        self.length = max(len(ds) for ds in self.datasets)\n\n    def __len__(self):\n        \"\"\"Get the size of the dataset.\"\"\"\n        return self.length\n\n    def __getitem__(self, idx):\n        \"\"\"Given index, sample the data from multiple datasets with the given\n        proportion.\"\"\"\n        p = np.random.rand()\n        for i in range(len(self.datasets)):\n            if p <= self.partition[i]:\n                index_new = (idx + np.random.rand()) * len(\n                    self.datasets[i]) / self.length\n                index_new = int(np.round(index_new)) % (len(self.datasets[i]))\n                return self.datasets[i][index_new]\n        return None\n", "path": "mmpose/datasets/datasets/mesh/mesh_mix_dataset.py"}]}