Address review comments.

Author: dan-zheng

Sources/TensorFlow/Initializers.swift

@@ -449,7 +449,7 @@ public extension Tensor where Scalar: BinaryFloatingPoint,
     }
 }
 
-fileprivate extension Tensor where Scalar: BinaryFloatingPoint {
+fileprivate extension Tensor where Scalar: TensorFlowFloatingPoint {
     private static func glorot(
         fromStandardUniform randomUniform: __shared Tensor<Scalar>,
         shape: __shared TensorShape
@@ -459,12 +459,7 @@ fileprivate extension Tensor where Scalar: BinaryFloatingPoint {
         let fanIn = shape[shape.count - 2] * receptiveField
         let fanOut = shape[shape.count - 1] * receptiveField
         let minusOneToOne = 2 * randomUniform - 1
-#if os(macOS) || os(iOS) || os(watchOS) || os(tvOS)
-        let _sqrt = Darwin.sqrt as (Scalar) -> Scalar
-#else
-        let _sqrt = Glibc.sqrt as (Scalar) -> Scalar
-#endif
-        return _sqrt(Scalar(6) / Scalar(fanIn + fanOut)) * minusOneToOne
+        return Scalar.sqrt(Scalar(6) / Scalar(fanIn + fanOut)) * minusOneToOne
     }
 }
 
@@ -488,8 +483,7 @@ public extension Tensor where Scalar: TensorFlowFloatingPoint {
     }
 }
 
-public extension Tensor where Scalar: BinaryFloatingPoint,
-                              Scalar.RawSignificand: FixedWidthInteger {
+public extension Tensor where Scalar: TensorFlowFloatingPoint {
     /// Performs Glorot uniform initialization for the specified shape, creating a tensor by
     /// randomly sampling scalar values from a uniform distribution between `-limit` and `limit`,
     /// where limit is `sqrt(6 / (fanIn + fanOut))` and `fanIn`/`fanOut` represent the number of

Sources/TensorFlow/Operators/Math.swift

@@ -132,7 +132,7 @@ extension Tensor: ElementaryFunctions where Scalar: TensorFlowFloatingPoint {
         TensorFlow.log1p(x)
     }
 
-    /// `x**y` interpreted as `exp(y * log(x))`
+    /// `exp(y log(x))` computed without loss of intermediate precision.
     ///
     /// For real types, if `x` is negative the result is NaN, even if `y` has
     /// an integral value. For complex types, there is a branch cut on the