Editorial: Format explanatory subscripts to emphasize variables

Instead of "*foo[bar]*" make it "*foo*[*bar*]" just so that turning
"foo" and "bar" into links is a more atomic operation. Purely
stylistic.

Part of webmachinelearning#783

docs/SpecCodingConventions.md

@@ -96,6 +96,7 @@ Example:
 * Format explanatory expressions using backticks, e.g. `` `max(0, x) + alpha * (exp(min(0, x)) - 1)` ``
 * In Web IDL `<pre class=idl>` blocks, wrap long lines to avoid horizontal scrollbars. 88 characters seems to be the magic number.
 * Avoid `<var>v</var>` or `|v|` outside of algorithms; Bikeshed interprets these as global variables which can mask errors. Just use `*v*`.
+    * Format each term separately; that is, `*splits*[*i*]` not `*splits[i]*`.
 
 
 ### Algorithms

index.bs

@@ -6152,8 +6152,8 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input tensor.
-        - <dfn>beginningPadding</dfn>: [=sequence=]<{{unsigned long}}>. The number of padding values to add at the beginning of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *beginningPadding[d]* indicates how many values to add before the content in that dimension.
-        - <dfn>endingPadding</dfn>: [=sequence=]<{{unsigned long}}>. The number of padding values to add at the ending of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *endingPadding[d]* indicates how many values to add after the content in that dimension.
+        - <dfn>beginningPadding</dfn>: [=sequence=]<{{unsigned long}}>. The number of padding values to add at the beginning of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *beginningPadding*[*d*] indicates how many values to add before the content in that dimension.
+        - <dfn>endingPadding</dfn>: [=sequence=]<{{unsigned long}}>. The number of padding values to add at the ending of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *endingPadding*[*d*] indicates how many values to add after the content in that dimension.
         - <dfn>options</dfn>: an optional {{MLPadOptions}}. The optional parameters of the operation.
 
     **Returns:** an {{MLOperand}}. The padded output tensor. Each dimension of the output tensor can be calculated as follows:
@@ -7248,8 +7248,8 @@ partial  dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/slice(input, starts, sizes, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input tensor.
-        - <dfn>starts</dfn>: [=sequence=]<{{unsigned long}}>. The starting index to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *starts[d]* indicates the starting index to slice in that dimension. The starting index must be in the range [0, input size - 1] in that dimension.
-        - <dfn>sizes</dfn>: [=sequence=]<{{unsigned long}}>. The number of elements to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *sizes[d]* indicates the number of elements to slice in that dimension. The size must not be 0 and must satisfy the constraint `starting index + size <= input size` in that dimension.
+        - <dfn>starts</dfn>: [=sequence=]<{{unsigned long}}>. The starting index to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *starts*[*d*] indicates the starting index to slice in that dimension. The starting index must be in the range [0, input size - 1] in that dimension.
+        - <dfn>sizes</dfn>: [=sequence=]<{{unsigned long}}>. The number of elements to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *sizes*[*d*] indicates the number of elements to slice in that dimension. The size must not be 0 and must satisfy the constraint `starting index + size <= input size` in that dimension.
         - <dfn>options</dfn>: an {{MLOperatorOptions}}. Specifies the optional parameters of the operation.
 
     **Returns:** an {{MLOperand}}. The output tensor of the same rank as the input tensor with tensor values stripped to the specified starting and ending indices in each dimension.
@@ -7585,7 +7585,7 @@ partial dictionary MLOpSupportLimits {
         - <dfn>splits</dfn>: an {{unsigned long}} or [=sequence=]<{{unsigned long}}>. If an {{unsigned long}}, it specifies the number of output tensors along the axis. The number must evenly divide the dimension size of *input* along *options*.{{MLSplitOptions/axis}}. If a [=sequence=]<{{unsigned long}}>, it specifies the sizes of each output tensor along the *options*.{{MLSplitOptions/axis}}. The sum of sizes must equal to the dimension size of *input* along *options*.{{MLSplitOptions/axis}}.
         - <dfn>options</dfn>: an optional {{MLSplitOptions}}. The optional parameters of the operation.
 
-    **Returns:** [=sequence=]<{{MLOperand}}>. The split output tensors. If *splits* is an {{unsigned long}}, the [=list/size=] of the output is equal to *splits*. The shape of each output tensor is the same as *input* except the dimension size of *axis* equals to the quotient of dividing the dimension size of *input* along *axis* by *splits*. If *splits* is a [=sequence=]<{{unsigned long}}>, the [=list/size=] of the output equals the [=list/size=] of *splits*. The shape of the i-th output tensor is the same as *input* except along *axis* where the dimension size is *splits[i]*.
+    **Returns:** [=sequence=]<{{MLOperand}}>. The split output tensors. If *splits* is an {{unsigned long}}, the [=list/size=] of the output is equal to *splits*. The shape of each output tensor is the same as *input* except the dimension size of *axis* equals to the quotient of dividing the dimension size of *input* along *axis* by *splits*. If *splits* is a [=sequence=]<{{unsigned long}}>, the [=list/size=] of the output equals the [=list/size=] of *splits*. The shape of the *i*-th output tensor is the same as *input* except along *axis* where the dimension size is *splits*[*i*].
 </div>
 
 {{MLSplitOptions}} has the following members: