Address PR #1529 review comments

- Add eps clamping documentation to torch.logit_ XML docs
- Add torch.i0_() static wrapper method with docs
- Add test for torch.i0_() static method

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Author: alinpahontu2912

src/TorchSharp/Tensor/torch.PointwiseOps.cs

@@ -1109,7 +1109,10 @@ public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long[]
         // https://pytorch.org/docs/stable/generated/torch.logit
         /// <summary>
         /// Returns the logit of the elements of input, in-place.
+        /// input is clamped to [eps, 1 - eps] when eps is not null.
         /// </summary>
+        /// <param name="input">The input tensor.</param>
+        /// <param name="eps">The epsilon for input clamp bound.</param>
         public static Tensor logit_(Tensor input, double? eps = null) => input.logit_(eps);
 
         // https://pytorch.org/docs/stable/generated/torch.hypot
@@ -1128,6 +1131,13 @@ public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long[]
         /// <returns></returns>
         [Pure]public static Tensor i0(Tensor input) => special.i0(input);
 
+        // https://pytorch.org/docs/stable/generated/torch.i0
+        /// <summary>
+        /// Computes the zeroth order modified Bessel function of the first kind for each element of input, in-place.
+        /// </summary>
+        /// <param name="input">The input tensor.</param>
+        public static Tensor i0_(Tensor input) => input.i0_();
+
         // https://pytorch.org/docs/stable/generated/torch.igamma
         /// <summary>
         /// Alias for <see cref="torch.special.gammainc"/>.

test/TorchSharpTest/PointwiseTensorMath.cs

@@ -929,6 +929,17 @@ public void I0InPlaceTest()
             Assert.True(res.allclose(torch.tensor(expected)));
         }
 
+        [Fact]
+        [TestOf(nameof(torch.i0_))]
+        public void I0InPlaceTorchTest()
+        {
+            var data = torch.arange(0, 5, 1, float32);
+            var expected = new float[] { 0.99999994f, 1.266066f, 2.27958512f, 4.88079262f, 11.3019209f };
+            var res = torch.i0_(data);
+            Assert.Same(data, res);
+            Assert.True(res.allclose(torch.tensor(expected)));
+        }
+
         [Fact]
         [TestOf(nameof(Tensor.hypot))]
         public void HypotTest()