We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Sync naming convention and style in NLP datasets
Also fixes a possible bug with np.load()/f.close() pair not being exception-safe.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `keras/datasets/boston_housing.py`
Content:
```
1 """Boston housing price regression dataset.
2 """
3 from __future__ import absolute_import
4 from __future__ import division
5 from __future__ import print_function
6 
7 from ..utils.data_utils import get_file
8 import numpy as np
9 
10 
11 def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
12     """Loads the Boston Housing dataset.
13 
14     # Arguments
15         path: path where to cache the dataset locally
16             (relative to ~/.keras/datasets).
17         test_split: fraction of the data to reserve as test set.
18         seed: Random seed for shuffling the data
19             before computing the test split.
20 
21     # Returns
22         Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
23     """
24     assert 0 <= test_split < 1
25     path = get_file(path,
26                     origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
27                     file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
28     f = np.load(path)
29     x = f['x']
30     y = f['y']
31     f.close()
32 
33     np.random.seed(seed)
34     indices = np.arange(len(x))
35     np.random.shuffle(indices)
36     x = x[indices]
37     y = y[indices]
38 
39     x_train = np.array(x[:int(len(x) * (1 - test_split))])
40     y_train = np.array(y[:int(len(x) * (1 - test_split))])
41     x_test = np.array(x[int(len(x) * (1 - test_split)):])
42     y_test = np.array(y[int(len(x) * (1 - test_split)):])
43     return (x_train, y_train), (x_test, y_test)
44
```
--- 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/keras/datasets/boston_housing.py b/keras/datasets/boston_housing.py
--- a/keras/datasets/boston_housing.py
+++ b/keras/datasets/boston_housing.py
@@ -25,10 +25,9 @@
     path = get_file(path,
                     origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
                     file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
-    f = np.load(path)
-    x = f['x']
-    y = f['y']
-    f.close()
+    with np.load(path) as f:
+        x = f['x']
+        y = f['y']
 
     np.random.seed(seed)
     indices = np.arange(len(x))

{"golden_diff": "diff --git a/keras/datasets/boston_housing.py b/keras/datasets/boston_housing.py\n--- a/keras/datasets/boston_housing.py\n+++ b/keras/datasets/boston_housing.py\n@@ -25,10 +25,9 @@\n     path = get_file(path,\n                     origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',\n                     file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')\n-    f = np.load(path)\n-    x = f['x']\n-    y = f['y']\n-    f.close()\n+    with np.load(path) as f:\n+        x = f['x']\n+        y = f['y']\n \n     np.random.seed(seed)\n     indices = np.arange(len(x))\n", "issue": "Sync naming convention and style in NLP datasets\nAlso fixes a possible bug with np.load()/f.close() pair not being exception-safe.\n", "before_files": [{"content": "\"\"\"Boston housing price regression dataset.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom ..utils.data_utils import get_file\nimport numpy as np\n\n\ndef load_data(path='boston_housing.npz', test_split=0.2, seed=113):\n    \"\"\"Loads the Boston Housing dataset.\n\n    # Arguments\n        path: path where to cache the dataset locally\n            (relative to ~/.keras/datasets).\n        test_split: fraction of the data to reserve as test set.\n        seed: Random seed for shuffling the data\n            before computing the test split.\n\n    # Returns\n        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n    \"\"\"\n    assert 0 <= test_split < 1\n    path = get_file(path,\n                    origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',\n                    file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')\n    f = np.load(path)\n    x = f['x']\n    y = f['y']\n    f.close()\n\n    np.random.seed(seed)\n    indices = np.arange(len(x))\n    np.random.shuffle(indices)\n    x = x[indices]\n    y = y[indices]\n\n    x_train = np.array(x[:int(len(x) * (1 - test_split))])\n    y_train = np.array(y[:int(len(x) * (1 - test_split))])\n    x_test = np.array(x[int(len(x) * (1 - test_split)):])\n    y_test = np.array(y[int(len(x) * (1 - test_split)):])\n    return (x_train, y_train), (x_test, y_test)\n", "path": "keras/datasets/boston_housing.py"}], "after_files": [{"content": "\"\"\"Boston housing price regression dataset.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom ..utils.data_utils import get_file\nimport numpy as np\n\n\ndef load_data(path='boston_housing.npz', test_split=0.2, seed=113):\n    \"\"\"Loads the Boston Housing dataset.\n\n    # Arguments\n        path: path where to cache the dataset locally\n            (relative to ~/.keras/datasets).\n        test_split: fraction of the data to reserve as test set.\n        seed: Random seed for shuffling the data\n            before computing the test split.\n\n    # Returns\n        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n    \"\"\"\n    assert 0 <= test_split < 1\n    path = get_file(path,\n                    origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',\n                    file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')\n    with np.load(path) as f:\n        x = f['x']\n        y = f['y']\n\n    np.random.seed(seed)\n    indices = np.arange(len(x))\n    np.random.shuffle(indices)\n    x = x[indices]\n    y = y[indices]\n\n    x_train = np.array(x[:int(len(x) * (1 - test_split))])\n    y_train = np.array(y[:int(len(x) * (1 - test_split))])\n    x_test = np.array(x[int(len(x) * (1 - test_split)):])\n    y_test = np.array(y[int(len(x) * (1 - test_split)):])\n    return (x_train, y_train), (x_test, y_test)\n", "path": "keras/datasets/boston_housing.py"}]}