Bug fix: Link operator names to the MLGraphBuilder methods

[inexorabletash]

For a "reshape" reference a malformed IDL link was present as *reshape*}}. The other references are linked as appropriately to the builder references rather than just being styled text.

Documented this convention in SpecCodingConventions.md.

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[zolkis]

Personally I'd prefer linking and it should be default generic thing, not a specific convention for this spec.
This emphasis style looks like a leftover from early spec times with draft texts changing fast.

[anssiko]

Thanks, I support an update to the conventions & style guide doc.

[inexorabletash]

Updated with a note in the conventions doc.

[huningxin]

LGTM with questions, thanks much!

[inexorabletash]

I noticed a few more potential places this style could be applied. Should I roll these in too, or leave them alone?

@@ -923,12 +923,12 @@ interface MLActivation {
 </div>
 
 <div class="note">
-These activations function types are used to create other operations. One such use of this interface is for when an activation function is fused into another operation such as [[#api-mlgraphbuilder-conv2d]] or [[#api-mlgraphbuilder-batchnorm]] during a graph construction session. Such fused activation functions can provide a significant performance improvement when supported natively by the underlying implementation. This is intended as an optimization opportunity for implementers.
+These activations function types are used to create other operations. One such use of this interface is for when an activation function is fused into another operation such as {{MLGraphBuilder/conv2d()}} or {{MLGraphBuilder/batchNormalization()}} during a graph construction session. Such fused activation functions can provide a significant performance improvement when supported natively by the underlying implementation. This is intended as an optimization opportunity for implementers.
 </div>
 
 ### Creating {{MLActivation}} ### {#api-mlactivation-create}
 <div class="note">
-The {{MLActivation}} objects (including the ones passed as input to methods) are created by the methods of {{MLGraphBuilder}} and are identified by their name. The |options| dictionary is defined by those methods. The actual creation of the activation function e.g. a [[#api-mlgraphbuilder-sigmoid-method]] or [[#api-mlgraphbuilder-relu-method]] can then be deferred until when the rest of the graph is ready to connect with it such as during the construction of [[#api-mlgraphbuilder-conv2d]] for example.
+The {{MLActivation}} objects (including the ones passed as input to methods) are created by the methods of {{MLGraphBuilder}} and are identified by their name. The |options| dictionary is defined by those methods. The actual creation of the activation function e.g. a {{MLGraphBuilder/sigmoid()}} or {{MLGraphBuilder/relu()}} can then be deferred until when the rest of the graph is ready to connect with it such as during the construction of {{MLGraphBuilder/conv2d()}} for example.
 </div>
 
 <details open algorithm>
@@ -3674,7 +3674,7 @@ Create a named {{MLOperand}} based on a descriptor, that can be used as an input
 </details>
 
 ### instanceNormalization ### {#api-mlgraphbuilder-instancenorm}
-Normalize the input using [[Instance-Normalization]]. Unlike [[#api-mlgraphbuilder-batchnorm]] where the mean and variance values used in the normalization are computed across all the samples in the batch dimension while the model is trained, the mean and variance values used in the instance normalization are computed on the fly for each input feature of each individual sample in the batch.
+Normalize the input using [[Instance-Normalization]]. Unlike {{MLGraphBuilder/batchNormalization()}} where the mean and variance values used in the normalization are computed across all the samples in the batch dimension while the model is trained, the mean and variance values used in the instance normalization are computed on the fly for each input feature of each individual sample in the batch.
 
 <script type=idl>
 dictionary MLInstanceNormalizationOptions {
@@ -3776,7 +3776,7 @@ partial interface MLGraphBuilder {
 </div>
 
 ### layerNormalization ### {#api-mlgraphbuilder-layernorm}
-Normalize the input using [[Layer-Normalization]]. Unlike [[#api-mlgraphbuilder-batchnorm]] where the mean and variance values are computed across all the samples in the batch dimension while the model is trained, and in [[#api-mlgraphbuilder-instancenorm]] where the mean and variance values are computed on the fly for each input feature of each individual sample in the batch, the means and variance values of the layer normalization are computed on the fly across all the input features of each individual sample in the batch.
+Normalize the input using [[Layer-Normalization]]. Unlike {{MLGraphBuilder/batchNormalization()}} where the mean and variance values are computed across all the samples in the batch dimension while the model is trained, and in {{MLGraphBuilder/instanceNormalization()}} where the mean and variance values are computed on the fly for each input feature of each individual sample in the batch, the means and variance values of the layer normalization are computed on the fly across all the input features of each individual sample in the batch.
 
 <script type=idl>
 dictionary MLLayerNormalizationOptions {

[zolkis]

Roll'em.