diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py
index b8241f489e5..0ea02bdef1b 100644
--- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py
+++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py
@@ -93,7 +93,9 @@ def __init__(
         batch_size: int
             Batch size.
         sparse_format: str, default = "coo"
-            Sparse format of the sample graph. Choose between "csc" and "coo".
+            The sparse format of the emitted sampled graphs. Choose between "csc"
+            and "coo". When using "csc", the graphs are of type
+            cugraph_dgl.nn.SparseGraph.
         kwargs : dict
             Key-word arguments to be passed to the parent PyTorch
             :py:class:`torch.utils.data.DataLoader` class. Common arguments are:
diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py
index 26e33166d4e..3a16c6580d2 100644
--- a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py
+++ b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py
@@ -11,7 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import annotations
-from typing import List, Tuple, Dict, Optional, Any
+from typing import List, Tuple, Dict, Optional
 from collections import defaultdict
 import cudf
 import cupy
@@ -417,16 +417,31 @@ def _process_sampled_df_csc(
     Convert a dataframe generated by BulkSampler to a dictionary of tensors, to
     facilitate MFG creation. The sampled graphs in the dataframe use CSC-format.
 
+    Parameters
+    ----------
     df: cudf.DataFrame
-        The CSR output by BulkSampler.
-    reverse_hop_id: bool, default=True
+        The output from BulkSampler compressed in CSC format. The dataframe
+        should be generated with `compression="CSR"` in BulkSampler,
+        since the sampling routine treats seed nodes as sources.
+
+    reverse_hop_id: bool (default=True)
         Reverse hop id.
 
-    Returns:
-        tensor_dict[batch_id][hop_id] has three keys:
-        - src_ids:
-        - cdst_ids:
-        - mfg_size:
+    Returns
+    -------
+    tensors_dict: dict
+        A nested dictionary keyed by batch id and hop id.
+        `tensor_dict[batch_id][hop_id]` holds "minors" and "major_offsets"
+        values for CSC MFGs.
+
+    renumber_map_list: list
+        List of renumbering maps for looking up global indices of nodes. One
+        map for each batch.
+
+    mfg_sizes: list
+        List of the number of nodes in each message passing layer. For the
+        k-th hop, mfg_sizes[k] and mfg_sizes[k+1] is the number of sources and
+        destinations, respectively.
     """
     # dropna
     major_offsets = df.major_offsets.dropna().values
@@ -495,9 +510,16 @@ def _create_homogeneous_sparse_graphs_from_csc(
     tensors_dict: Dict[int, Dict[int, Dict[str, torch.Tensor]]],
     renumber_map_list: List[torch.Tensor],
     mfg_sizes: List[int, int],
-) -> Any:
-    """Create mini-batches of MFGs. The input argument are the outputs of
-    the function `_process_sampled_df_csc`."""
+) -> List[List[torch.Tensor, torch.Tensor, List[SparseGraph]]]:
+    """Create mini-batches of MFGs. The input arguments are the outputs of
+    the function `_process_sampled_df_csc`.
+
+    Returns
+    -------
+    output: list
+        A list of mini-batches. Each mini-batch is a list that consists of
+        `input_nodes` tensor, `output_nodes` tensor and a list of MFGs.
+    """
     n_batches, n_hops = len(mfg_sizes), len(mfg_sizes[0]) - 1
     output = []
     for b_id in range(n_batches):
@@ -523,6 +545,8 @@ def _create_homogeneous_sparse_graphs_from_csc(
 
 
 def create_homogeneous_sampled_graphs_from_dataframe_csc(sampled_df: cudf.DataFrame):
+    """Public API to create mini-batches of MFGs using a dataframe output by
+    BulkSampler, where the sampled graph is compressed in CSC format."""
     return _create_homogeneous_sparse_graphs_from_csc(
         *(_process_sampled_df_csc(sampled_df))
     )