diff --git a/graphlearn_torch/python/distributed/dist_neighbor_loader.py b/graphlearn_torch/python/distributed/dist_neighbor_loader.py
index 9eae94fd..9d93d8e8 100644
--- a/graphlearn_torch/python/distributed/dist_neighbor_loader.py
+++ b/graphlearn_torch/python/distributed/dist_neighbor_loader.py
@@ -17,8 +17,14 @@
 
 import torch
 
-from ..sampler import NodeSamplerInput, SamplingType, SamplingConfig, \
-    RemoteNodeSplitSamplerInput, RemoteNodePathSamplerInput
+from ..sampler import (
+    AllNodeSamplerInputs,
+    NodeSamplerInput,
+    SamplingType,
+    SamplingConfig,
+    RemoteNodeSplitSamplerInput,
+    RemoteNodePathSamplerInput,
+)
 from ..typing import InputNodes, NumNeighbors, Split
 
 from .dist_dataset import DistDataset
@@ -27,7 +33,7 @@
 
 
 class DistNeighborLoader(DistLoader):
-  r""" A distributed loader that preform sampling from nodes.
+  r"""A distributed loader that preform sampling from nodes.
 
   Args:
     data (DistDataset, optional): The ``DistDataset`` object of a partition of
@@ -37,10 +43,14 @@ class DistNeighborLoader(DistLoader):
       The number of neighbors to sample for each node in each iteration.
       In heterogeneous graphs, may also take in a dictionary denoting
       the amount of neighbors to sample for each individual edge type.
-    input_nodes (torch.Tensor or Tuple[str, torch.Tensor]): The node seeds for
+    input_nodes (torch.Tensor or Tuple[str, torch.Tensor], optional): The node seeds for
       which neighbors are sampled to create mini-batches. In heterogeneous
       graphs, needs to be passed as a tuple that holds the node type and
       node seeds.
+      Must *not* be provided when ``node_sampler_input`` is provided.
+    node_sampler_input: (AllNodeSamplerInputs, optional): The input for node sampler.
+      May be used to override the functionality when sampling.
+      Must *not* be provided when ``input_nodes`` is provided.
     batch_size (int): How many samples per batch to load (default: ``1``).
     shuffle (bool): Set to ``True`` to have the data reshuffled at every
       epoch (default: ``False``).
@@ -74,7 +84,8 @@ class DistNeighborLoader(DistLoader):
   def __init__(self,
                data: Optional[DistDataset],
                num_neighbors: NumNeighbors,
-               input_nodes: InputNodes,
+               input_nodes: Optional[InputNodes] = None,
+               node_sampler_input: Optional[AllNodeSamplerInputs] = None,
                batch_size: int = 1,
                shuffle: bool = False,
                drop_last: bool = False,
@@ -85,13 +96,19 @@ def __init__(self,
                to_device: Optional[torch.device] = None,
                random_seed: Optional[int] = None,
                worker_options: Optional[AllDistSamplingWorkerOptions] = None):
-
+    if (input_nodes is None) == (node_sampler_input is None):
+      raise ValueError(
+        f"Exactly one of input_nodes or node_sampler_input must be provided. Received input_nodes: {type(input_nodes)}, node_sampler_input: {type(node_sampler_input)}"
+      )
     if isinstance(input_nodes, tuple):
       input_type, input_seeds = input_nodes
-    else:
+    elif input_nodes is not None:
       input_type, input_seeds = None, input_nodes
 
-    if isinstance(worker_options, RemoteDistSamplingWorkerOptions):
+    if node_sampler_input is not None:
+      _check_input_type(node_sampler_input, worker_options)
+      input_data = node_sampler_input
+    elif isinstance(worker_options, RemoteDistSamplingWorkerOptions):
       if isinstance(input_seeds, Split):
         input_data = RemoteNodeSplitSamplerInput(split=input_seeds, input_type=input_type)
         if isinstance(worker_options.server_rank, List):
@@ -113,6 +130,29 @@ def __init__(self,
       with_weight=with_weight, edge_dir=edge_dir, seed=random_seed
     )
 
-    super().__init__(
-      data, input_data, sampling_config, to_device, worker_options
-    )
+    super().__init__(data, input_data, sampling_config, to_device, worker_options)
+
+
+def _check_input_type(node_sampler_input: AllNodeSamplerInputs, worker_options: AllDistSamplingWorkerOptions):
+    if isinstance(worker_options, RemoteDistSamplingWorkerOptions):
+        if isinstance(worker_options.server_rank, List):
+            if not isinstance(node_sampler_input, List):
+                raise ValueError(
+                    f"When worker options is {type(RemoteDistSamplingWorkerOptions)} with server_rank as List, node_sampler_input must be a list, but got {type(node_sampler_input)}"
+                )
+            if not all(
+                isinstance(sampler_input, RemoteNodeSplitSamplerInput)
+                for sampler_input in node_sampler_input
+            ):
+                raise ValueError(
+                    f"When worker options is RemoteDistSamplingWorkerOptions with server_rank as List, node_sampler_input must be a list and all elements of the list must be of type RemoteNodePathSamplerInput, but got {[type(nsi) for nsi in node_sampler_input]}"
+                )
+        elif isinstance(node_sampler_input, List):
+          if not all(isinstance(sampler_input, RemoteNodePathSamplerInput) for sampler_input in node_sampler_input):
+            raise ValueError(
+                f"When worker options is {type(worker_options)} and node_sampler_input is a list, all elements of the list must be of type RemoteNodePathSamplerInput, but got {[type(nsi) for nsi in node_sampler_input]}"
+            )
+        elif not isinstance(node_sampler_input, RemoteNodePathSamplerInput):  
+            raise ValueError(
+                f"When worker options is {type(worker_options)} and node_sampler_input is not a list, node_sampler_input must be of type RemoteNodePathSamplerInput, but got {type(node_sampler_input)}"
+            )
\ No newline at end of file
diff --git a/graphlearn_torch/python/sampler/base.py b/graphlearn_torch/python/sampler/base.py
index 5b831b3b..e416085e 100644
--- a/graphlearn_torch/python/sampler/base.py
+++ b/graphlearn_torch/python/sampler/base.py
@@ -24,6 +24,8 @@
 from ..typing import NodeType, EdgeType, NumNeighbors, Split
 from ..utils import CastMixin
 
+AllNodeSamplerInputsBase = Union['NodeSamplerInput', 'RemoteNodeSplitSamplerInput', 'RemoteNodePathSamplerInput']
+AllNodeSamplerInputs = Union[AllNodeSamplerInputsBase, List['RemoteNodePathSamplerInput'], List['RemoteNodeSplitSamplerInput']]
 
 class EdgeIndex(NamedTuple):
   r""" PyG's :class:`~torch_geometric.loader.EdgeIndex` used in old data loader