Try to fix spacing/formatting in returns section of Keras dataset docs.

For an example of where it's broken, see: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/boston_housing/load_data?hl=en&version=nightly

PiperOrigin-RevId: 292604843
Change-Id: I8425808b211306bf60ff5a58beeedb11c01f7a2f

tensorflow/python/keras/datasets/boston_housing.py

@@ -48,9 +48,10 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: numpy arrays with shape (num_samples, 13) containing
+      **x_train, x_test**: numpy arrays with shape (num_samples, 13) containing
         either the training samples (for x_train), or test samples (for y_train)
-      y_train, y_test: numpy arrays of shape (num_samples, ) containing the
+
+      **y_train, y_test**: numpy arrays of shape (num_samples, ) containing the
         target scalars. The targets are float scalars typically between 10 and
         50 that represent the home prices in k$.
   """

tensorflow/python/keras/datasets/cifar10.py

@@ -39,12 +39,13 @@ def load_data():
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: uint8 arrays of RGB image data with shape
+      **x_train, x_test**: uint8 arrays of RGB image data with shape
         (num_samples, 3, 32, 32) if the `tf.keras.backend.image_data_format` is
         'channels_first', or (num_samples, 32, 32, 3) if the data format
         is 'channels_last'.
-      y_train, y_test: uint8 arrays of category labels (integers in range 0-9)
-        each with shape (num_samples, 1).
+
+      **y_train, y_test**: uint8 arrays of category labels
+        (integers in range 0-9) each with shape (num_samples, 1).
   """
   dirname = 'cifar-10-batches-py'
   origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

tensorflow/python/keras/datasets/cifar100.py

@@ -45,11 +45,12 @@ def load_data(label_mode='fine'):
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: uint8 arrays of RGB image data with shape
+      **x_train, x_test**: uint8 arrays of RGB image data with shape
         (num_samples, 3, 32, 32) if the `tf.keras.backend.image_data_format` is
         'channels_first', or (num_samples, 32, 32, 3) if the data format
         is 'channels_last'.
-      y_train, y_test: uint8 arrays of category labels with shape
+
+      **y_train, y_test**: uint8 arrays of category labels with shape
         (num_samples, 1).
 
   Raises:

tensorflow/python/keras/datasets/fashion_mnist.py

@@ -51,9 +51,10 @@ def load_data():
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: uint8 arrays of grayscale image data with shape
+      **x_train, x_test**: uint8 arrays of grayscale image data with shape
         (num_samples, 28, 28).
-      y_train, y_test: uint8 arrays of labels (integers in range 0-9)
+
+      **y_train, y_test**: uint8 arrays of labels (integers in range 0-9)
         with shape (num_samples,).
 
   License:

tensorflow/python/keras/datasets/imdb.py

@@ -78,11 +78,12 @@ def load_data(path='imdb.npz',
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: lists of sequences, which are lists of indexes
+      **x_train, x_test**: lists of sequences, which are lists of indexes
         (integers). If the num_words argument was specific, the maximum
         possible index value is num_words-1. If the `maxlen` argument was
         specified, the largest possible sequence length is `maxlen`.
-      y_train, y_test: lists of integer labels (1 or 0).
+
+      **y_train, y_test**: lists of integer labels (1 or 0).
 
   Raises:
       ValueError: in case `maxlen` is so low

tensorflow/python/keras/datasets/mnist.py

@@ -41,9 +41,10 @@ def load_data(path='mnist.npz'):
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: uint8 arrays of grayscale image data with shapes
+      **x_train, x_test**: uint8 arrays of grayscale image data with shapes
         (num_samples, 28, 28).
-      y_train, y_test: uint8 arrays of digit labels (integers in range 0-9)
+
+      **y_train, y_test**: uint8 arrays of digit labels (integers in range 0-9)
         with shapes (num_samples,).
 
   License:

tensorflow/python/keras/datasets/reuters.py

@@ -89,11 +89,12 @@ def load_data(path='reuters.npz',
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      x_train, x_test: lists of sequences, which are lists of indexes
+      **x_train, x_test**: lists of sequences, which are lists of indexes
         (integers). If the num_words argument was specific, the maximum
         possible index value is num_words-1. If the `maxlen` argument was
         specified, the largest possible sequence length is `maxlen`.
-      y_train, y_test: lists of integer labels (1 or 0).
+
+      **y_train, y_test**: lists of integer labels (1 or 0).
 
   Note: The 'out of vocabulary' character is only used for
   words that were present in the training set but are not included