typo

torch_scatter/functions/scatter.py

@@ -6,7 +6,7 @@ from torch.autograd import Function
 from .._ext import ffi
 
 
-def _has_output_index(name):
+def has_output_index(name):
     return name in ['max', 'min']
 
 
@@ -35,10 +35,10 @@ def _scatter(name, dim, *data):
     typename = type(data[0]).__name__.replace('Tensor', '')
     func = getattr(ffi, 'scatter_{}_{}'.format(name, typename))
     func(dim, *data)
-    return (data[0], data[3]) if _has_output_index(name) else data[0]
+    return (data[0], data[3]) if has_output_index(name) else data[0]
 
 
-def _index_backward(dim, index, grad, grad_index):
+def index_backward(dim, index, grad, grad_index):
     typename = type(grad).__name__.replace('Tensor', '')
     func = getattr(ffi, 'index_backward_{}'.format(typename))
     output = grad.new(index.size()).fill_(0)
@@ -63,7 +63,7 @@ class _Scatter(Function):
         # `scatter_min` and `scatter_max` additionally return the `argmax`
         # respectively `argmin`. In addition, we need to save the
         # `output_index` for the backward pass.
-        if _has_output_index(self.name):
+        if has_output_index(self.name):
             self.save_for_backward(data[1], data[3])
             return data[0], data[3]
         else:
@@ -78,14 +78,14 @@ class _Scatter(Function):
 
         # Different grad computation of `input` if `scatter_max` or
         # `scatter_min` was used.
-        if self.needs_input_grad[2] and not _has_output_index(self.name):
+        if self.needs_input_grad[2] and not has_output_index(self.name):
             index, = self.saved_variables
             grad_input = data[0].gather(self.dim, index.data)
 
-        if self.needs_input_grad[2] and _has_output_index(self.name):
+        if self.needs_input_grad[2] and has_output_index(self.name):
             index, grad_index = self.saved_variables
             data = (index.data, data[0], grad_index.data)
-            grad_input = _index_backward(self.dim, *data)
+            grad_input = index_backward(self.dim, *data)
 
         # Return and fill with empty grads for none-differentiable passed
         # arguments in forward pass.