Remove warnings generated in test_graphmask_explainer.py and `test_…

…laplacian_lambda_max.py`. (#9179)

With this fix, the following warning:
```
test/explain/algorithm/test_graphmask_explainer.py: 12960 warnings
  /usr/local/lib/python3.10/dist-packages/torch_geometric/explain/algorithm/graphmask_explainer.py:165: DeprecationWarning: `np.math` is a deprecated alias for the standard library `math` module (Deprecated Numpy 1.25). Replace usages of `np.math` with `math`
    beta * np.math.log(-gamma / zeta))


test/transforms/test_laplacian_lambda_max.py::test_laplacian_lambda_max
test/transforms/test_laplacian_lambda_max.py::test_laplacian_lambda_max
test/transforms/test_laplacian_lambda_max.py::test_laplacian_lambda_max
test/transforms/test_laplacian_lambda_max.py::test_laplacian_lambda_max
  /usr/local/lib/python3.10/dist-packages/torch_geometric/transforms/laplacian_lambda_max.py:65: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
    data.lambda_max = float(lambda_max.real)
```
will no longer be generated.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>

torch_geometric/explain/algorithm/graphmask_explainer.py

@@ -1,7 +1,6 @@
 import math
 from typing import List, Optional, Tuple, Union
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 from torch import Tensor
@@ -162,7 +161,7 @@ def _hard_concrete(
                 (torch.log(u) - torch.log(1 - u) + input_element) / beta)
 
             penalty = torch.sigmoid(input_element -
-                                    beta * np.math.log(-gamma / zeta))
+                                    beta * math.log(-gamma / zeta))
         else:
             s = torch.sigmoid(input_element)
             penalty = torch.zeros_like(input_element)

torch_geometric/transforms/laplacian_lambda_max.py

@@ -62,7 +62,7 @@ def forward(self, data: Data) -> Data:
             eig_fn = eigsh
 
         lambda_max = eig_fn(L, k=1, which='LM', return_eigenvectors=False)
-        data.lambda_max = float(lambda_max.real)
+        data.lambda_max = lambda_max.real.item()
 
         return data