eye func

test/test_eye.py

+from torch_sparse import eye
+
+
+def test_eye():
+    index, value = eye(3)
+    assert index.tolist() == [[0, 1, 2], [0, 1, 2]]
+    assert value.tolist() == [1, 1, 1]

torch_sparse/__init__.py

 from .coalesce import coalesce
 from .transpose import transpose
+from .eye import eye
 from .spmm import spmm
 from .spspmm import spspmm
 
@@ -9,6 +10,7 @@ __all__ = [
     '__version__',
     'coalesce',
     'transpose',
+    'eye',
     'spmm',
     'spspmm',
 ]

torch_sparse/eye.py

+import torch
+
+
+def eye(m, dtype=None, device=None):
+    """Returns a sparse matrix with ones on the diagonal and zeros elsewhere.
+
+    Args:
+        m (int): The first dimension of sparse matrix.
+        dtype (`torch.dtype`, optional): The desired data type of returned
+            value vector. (default is set by `torch.set_default_tensor_type()`)
+        device (`torch.device`, optional): The desired device of returned
+            tensors. (default is set by `torch.set_default_tensor_type()`)
+
+    :rtype: (:class:`LongTensor`, :class:`Tensor`)
+    """
+
+    row = torch.arange(m, dtype=torch.long, device=device)
+    index = torch.stack([row, row], dim=0)
+
+    value = torch.ones(m, dtype=dtype, device=device)
+
+    return index, value