add back gelu

index.bs

@@ -2755,6 +2755,7 @@ Compute the <a href="https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#G
 <script type=idl>
 partial interface MLGraphBuilder {
   MLOperand gelu(MLOperand input);
+  MLActivation gelu();
 };
 </script>
 
@@ -2798,6 +2799,23 @@ partial interface MLGraphBuilder {
     1. Return |output|.
 </details>
 
+#### {{MLGraphBuilder/gelu()}} #### {#api-mlgraphbuilder-gelu}
+<div>
+    **Arguments:**
+        - None.
+
+    **Returns:**
+        - an {{MLActivation}}. The activation function representing the gelu operation.
+</div>
+
+<details open algorithm>
+  <summary>
+    The <dfn method for=MLGraphBuilder id=gelu-noargs>gelu()</dfn> method steps are:
+  </summary>
+    1. Let |op| be the result of [=creating an MLActivation=] given [=this=] and "gelu".
+    1. Return |op|.
+</details>
+
 ### gemm ### {#api-mlgraphbuilder-gemm}
 Calculate the [general matrix multiplication of the Basic Linear Algebra Subprograms](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3). The calculation follows the expression `alpha * A * B + beta * C`, where `A` is a 2-D tensor with shape [M, K] or [K, M], `B` is a 2-D tensor with shape [K, N] or [N, K], and `C` is [=unidirectionally broadcastable=] to the shape [M, N]. `A` and `B` may optionally be transposed prior to the calculation.