docs(datasets) Fix docs examples formatting (#3702)

datasets/flwr_datasets/federated_dataset.py

@@ -85,6 +85,7 @@ class FederatedDataset:
     >>> centralized = fds.load_split("test")
 
     Use CIFAR10 dataset for Federated Laerning with 100 clients:
+
     >>> from flwr_datasets import FederatedDataset
     >>> from flwr_datasets.partitioner import DirichletPartitioner
     >>>
@@ -93,7 +94,8 @@ class FederatedDataset:
     >>> fds = FederatedDataset(dataset="cifar10", partitioners={"train": partitioner})
     >>> partition = fds.load_partition(partition_id=0)
 
-    Visualize the partitioned datasets
+    Visualize the partitioned datasets:
+
     >>> from flwr_datasets.visualization import plot_label_distributions
     >>>
     >>> _ = plot_label_distributions(

datasets/flwr_datasets/partitioner/dirichlet_partitioner.py

@@ -78,7 +78,7 @@ class DirichletPartitioner(Partitioner):
     >>> print(partition[0])  # Print the first example
     {'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x127B92170>,
     'label': 4}
-    >>> partition_sizes = partition_sizes = [
+    >>> partition_sizes = [
     >>>     len(fds.load_partition(partition_id)) for partition_id in range(10)
     >>> ]
     >>> print(sorted(partition_sizes))

datasets/flwr_datasets/partitioner/exponential_partitioner.py

@@ -35,6 +35,15 @@ class ExponentialPartitioner(SizePartitioner):
     ----------
     num_partitions : int
         The total number of partitions that the data will be divided into.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ExponentialPartitioner
+    >>>
+    >>> partitioner = ExponentialPartitioner(num_partitions=10)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
     """
 
     def __init__(self, num_partitions: int) -> None:

datasets/flwr_datasets/partitioner/iid_partitioner.py

@@ -26,6 +26,15 @@ class IidPartitioner(Partitioner):
     ----------
     num_partitions : int
         The total number of partitions that the data will be divided into.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import IidPartitioner
+    >>>
+    >>> partitioner = IidPartitioner(num_partitions=10)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
     """
 
     def __init__(self, num_partitions: int) -> None:

datasets/flwr_datasets/partitioner/linear_partitioner.py

@@ -29,6 +29,15 @@ class LinearPartitioner(SizePartitioner):
     ----------
     num_partitions : int
         The total number of partitions that the data will be divided into.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import LinearPartitioner
+    >>>
+    >>> partitioner = LinearPartitioner(num_partitions=10)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
     """
 
     def __init__(self, num_partitions: int) -> None:

datasets/flwr_datasets/partitioner/natural_id_partitioner.py

@@ -37,6 +37,7 @@ class NaturalIdPartitioner(Partitioner):
     Examples
     --------
     "flwrlabs/shakespeare" dataset
+
     >>> from flwr_datasets import FederatedDataset
     >>> from flwr_datasets.partitioner import NaturalIdPartitioner
     >>>
@@ -46,6 +47,7 @@ class NaturalIdPartitioner(Partitioner):
     >>> partition = fds.load_partition(0)
 
     "sentiment140" (aka Twitter) dataset
+
     >>> from flwr_datasets import FederatedDataset
     >>> from flwr_datasets.partitioner import NaturalIdPartitioner
     >>>

datasets/flwr_datasets/partitioner/square_partitioner.py

@@ -31,6 +31,15 @@ class SquarePartitioner(SizePartitioner):
     ----------
     num_partitions : int
         The total number of partitions that the data will be divided into.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import SquarePartitioner
+    >>>
+    >>> partitioner = SquarePartitioner(num_partitions=10)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
     """
 
     def __init__(self, num_partitions: int) -> None: