Squashed commit of the following:

commit 0a3ec8f
Author: GreatRSingh <rakshitsingh421@gmail.com>
Date:   Fri Jan 19 17:33:57 2024 +0530

    sibling test

commit 2b83c5f
Author: GreatRSingh <rakshitsingh421@gmail.com>
Date:   Fri Jan 19 16:55:51 2024 +0530

    transform tests

deepchem/utils/differentiation_utils/pure_function.py

@@ -360,6 +360,7 @@ def _check_identical_objs(objs1: List, objs2: List) -> bool:
             return False
     return True
 
+
 class SingleSiblingPureFunction(PureFunction):
     """Implementation of PureFunction for a sibling method
 
@@ -465,7 +466,6 @@ def _set_all_obj_params(self, allobjparams: List):
                 allobjparams[self.cumsum_idx[i]:self.cumsum_idx[i + 1]])
 
 
-
 def get_pure_function(fcn) -> PureFunction:
     """Get the pure function form of the function or method ``fcn``.
 
@@ -530,6 +530,22 @@ def make_sibling(*pfuncs) -> Callable[[Callable], PureFunction]:
     Changing the state of the decorated function will also change the state of
     ``pfunc`` and its other siblings.
 
+    Examples
+    --------
+    >>> import torch
+    >>> from deepchem.utils.differentiation_utils import make_sibling
+    >>> def fcn1(x, y):
+    ...     return x + y
+    >>> def fcn2(x, y):
+    ...     return x - y
+    >>> pfunc1 = get_pure_function(fcn1)
+    >>> pfunc2 = get_pure_function(fcn2)
+    >>> @make_sibling(pfunc1)
+    ... def fcn3(x, y):
+    ...     return x * y
+    >>> pfunc3(1, 2)
+    2
+
     Parameters
     ----------
     pfuncs: List[Callable]

deepchem/utils/pytorch_utils.py

@@ -436,7 +436,7 @@ def to_fortran_order(V):
             "Only the last two dimensions can be made Fortran order.")
 
 
-def get_np_dtype(dtype: torch.dtype) -> np.dtype:
+def get_np_dtype(dtype: torch.dtype) -> Any:
     """corresponding numpy dtype from the input pytorch's tensor dtype
 
     Examples

deepchem/utils/test/test_dft_utils.py

@@ -459,7 +459,7 @@ def test_radial_grid():
 def test_get_xw_integration():
     from deepchem.utils.dft_utils import get_xw_integration
     x, w = get_xw_integration(4, "chebyshev")
-    assert x.shape == (4, )
+    assert x.shape == (4,)
     assert w.shape == torch.Size([4])
 
 
@@ -472,6 +472,37 @@ def test_sliced_radial_grid():
 
 
 @pytest.mark.torch
-def test_base_grid_transform():
-    from deepchem.utils.dft_utils import BaseGridTransform
-
+def test_de2_transform():
+    from deepchem.utils.dft_utils import DE2Transformation
+    x = torch.linspace(-1, 1, 100)
+    r = DE2Transformation().x2r(x)
+    assert r.shape == torch.Size([100])
+    drdx = DE2Transformation().get_drdx(x)
+    assert drdx.shape == torch.Size([100])
+
+
+@pytest.mark.torch
+def test_logm3_transform():
+    from deepchem.utils.dft_utils import LogM3Transformation
+    x = torch.linspace(-1, 1, 100)
+    r = LogM3Transformation().x2r(x)
+    assert r.shape == torch.Size([100])
+    drdx = LogM3Transformation().get_drdx(x)
+    assert drdx.shape == torch.Size([100])
+
+
+@pytest.mark.torch
+def test_treutlerm4_transform():
+    from deepchem.utils.dft_utils import TreutlerM4Transformation
+    x = torch.linspace(-1, 1, 100)
+    r = TreutlerM4Transformation().x2r(x)
+    assert r.shape == torch.Size([100])
+    drdx = TreutlerM4Transformation().get_drdx(x)
+    assert drdx.shape == torch.Size([100])
+
+
+@pytest.mark.torch
+def test_get_grid_transform():
+    from deepchem.utils.dft_utils import get_grid_transform
+    transform = get_grid_transform("logm3")
+    transform.x2r(torch.tensor([0.5])) == torch.tensor([2.])

deepchem/utils/test/test_differentiation_utils.py

@@ -643,6 +643,20 @@ def fcn(x, y):
     assert pfunc(1, 2) == 3
 
 
+@pytest.mark.torch
+def test_make_siblings():
+    from deepchem.utils.differentiation_utils import make_sibling
+
+    def fcn1(x, y):
+        return x + y
+
+    @make_sibling(fcn1)
+    def fcn3(x, y):
+        return x * y
+
+    assert fcn3(1, 2) == 2
+
+
 @pytest.mark.torch
 def test_wrap_gmres():
     from deepchem.utils.differentiation_utils.solve import wrap_gmres