better knn/radius readme description [ci skip]

README.md

@@ -158,6 +158,16 @@ tensor([0, 3])
 
 Computes graph edges to the nearest *k* points.
 
+**Args:**
+
+* **x** *(Tensor)*: Node feature matrix of shape `[N, F]`.
+* **r** *(float)*: The radius.
+* **batch** *(LongTensor, optional)*: Batch vector of shape `[N]`, which assigns each node to a specific example. `batch` needs to be sorted. (default: `None`)
+* **loop** *(bool, optional)*: If `True`, the graph will contain self-loops. (default: `False`)
+* **flow** *(string, optional)*: The flow direction when using in combination with message passing (`"source_to_target"` or `"target_to_source"`). (default: `"source_to_target"`)
+* **cosine** *(boolean, optional)*: If `True`, will use the Cosine distance instead of Euclidean distance to find nearest neighbors. (default: `False`)
+* **num_workers** *(int)*: Number of workers to use for computation. Has no effect in case `batch` is not `None`, or the input lies on the GPU. (default: `1`)
+
 ```python
 import torch
 from torch_cluster import knn_graph
@@ -177,6 +187,16 @@ tensor([[1, 2, 0, 3, 0, 3, 1, 2],
 
 Computes graph edges to all points within a given distance.
 
+**Args:**
+
+* **x** *(Tensor)*: Node feature matrix of shape `[N, F]`.
+* **r** *(float)*: The radius.
+* **batch** *(LongTensor, optional)*: Batch vector of shape `[N]`, which assigns each node to a specific example. `batch` needs to be sorted. (default: `None`)
+* **loop** *(bool, optional)*: If `True`, the graph will contain self-loops. (default: `False`)
+* **max_num_neighbors** *(int, optional)*: The maximum number of neighbors to return for each element. (default: `32`)
+* **flow** *(string, optional)*: The flow direction when using in combination with message passing (`"source_to_target"` or `"target_to_source"`). (default: `"source_to_target"`)
+* **num_workers** *(int)*: Number of workers to use for computation. Has no effect in case `batch` is not `None`, or the input lies on the GPU. (default: `1`)
+
 ```python
 import torch
 from torch_cluster import radius_graph

torch_cluster/knn.py

@@ -92,11 +92,11 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
             (default: :obj:`None`)
         loop (bool, optional): If :obj:`True`, the graph will contain
             self-loops. (default: :obj:`False`)
-        flow (string, optional): The flow direction when using in combination
+        flow (string, optional): The flow direction when used in combination
             with message passing (:obj:`"source_to_target"` or
             :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
-        cosine (boolean, optional): If :obj:`True`, will use the cosine
-            distance instead of euclidean distance to find nearest neighbors.
+        cosine (boolean, optional): If :obj:`True`, will use the Cosine
+            distance instead of Euclidean distance to find nearest neighbors.
             (default: :obj:`False`)
         num_workers (int): Number of workers to use for computation. Has no
             effect in case :obj:`batch` is not :obj:`None`, or the input lies

torch_cluster/radius.py

@@ -91,8 +91,8 @@ def radius_graph(x: torch.Tensor, r: float,
         loop (bool, optional): If :obj:`True`, the graph will contain
             self-loops. (default: :obj:`False`)
         max_num_neighbors (int, optional): The maximum number of neighbors to
-            return for each element in :obj:`y`. (default: :obj:`32`)
-        flow (string, optional): The flow direction when using in combination
+            return for each element. (default: :obj:`32`)
+        flow (string, optional): The flow direction when used in combination
             with message passing (:obj:`"source_to_target"` or
             :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
         num_workers (int): Number of workers to use for computation. Has no