diff --git a/docs/SpecCodingConventions.md b/docs/SpecCodingConventions.md
index 52f4047c..9a5f5e72 100644
--- a/docs/SpecCodingConventions.md
+++ b/docs/SpecCodingConventions.md
@@ -149,5 +149,5 @@ Example:
 
 * Dictionary members are referenced using dotted property syntax. e.g. _options.padding_
    * Note that this is contrary to Web IDL + Infra; formally, a JavaScript object has been mapped to a Web IDL [dictionary](https://webidl.spec.whatwg.org/#idl-dictionaries) and then processed into an Infra [map](ordered) by the time a spec is using it. So formally the syntax _options["padding"]_ should be used.
-* Dictionary members should be linked to, both in algorithms and in other text. e.g. `|options|.{{MLOptionsDict/member}}` (in the steps for an algorithm) or `*options*.{{MLOptionsDict/member}}` (outside an algorithm).
+* Dictionary members should be linked to, both in algorithms and in other text. e.g. `|options|.{{MLOptionsDict/member}}` (in the steps for an algorithm) or `{{MLOptionsDict/member}}` (outside an algorithm).
 * Dictionary members should be given definitions somewhere in the text. This is usually done with a `<dl dfn-type=dict-member dfn-for=...>` for the dictionary as a whole, containing a `<dfn>` for each member.
diff --git a/index.bs b/index.bs
index b32fd68a..ee1be951 100644
--- a/index.bs
+++ b/index.bs
@@ -1767,7 +1767,7 @@ partial dictionary MLOpSupportLimits {
         - <dfn>axis</dfn>: The dimension to reduce. The value must be in the range [0, N-1] where N is the [=MLOperand/rank=] of the input tensor.
         - <dfn>options</dfn>: an optional {{MLArgMinMaxOptions}}. The optional parameters of the operation.
 
-    **Returns:** an {{MLOperand}}. The N-D tensor of the reduced shape. The values must be of type *options*.{{MLArgMinMaxOptions/outputDataType}} in the range [0, N-1] where N is the size of the input dimension specified by axis.
+    **Returns:** an {{MLOperand}}. The N-D tensor of the reduced shape. The values must be of type {{MLArgMinMaxOptions/outputDataType}} in the range [0, N-1] where N is the size of the input dimension specified by axis.
 </div>
 
 <table id=constraints-argMin-argMax class='data' link-for="MLGraphBuilder/argMin(input, axis, options), MLGraphBuilder/argMax(input, axis, options)">
@@ -2435,12 +2435,12 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/conv2d(input, filter, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 4-D tensor. The logical shape
-            is interpreted according to the value of *options*.{{MLConv2dOptions/inputLayout}}.
+            is interpreted according to the value of {{MLConv2dOptions/inputLayout}}.
         - <dfn>filter</dfn>: an {{MLOperand}}. The filter 4-D tensor. The logical shape is
-            interpreted according to the value of *options*.{{MLConv2dOptions/filterLayout}} and *options*.{{MLConv2dOptions/groups}}.
+            interpreted according to the value of {{MLConv2dOptions/filterLayout}} and {{MLConv2dOptions/groups}}.
         - <dfn>options</dfn>: an {{MLConv2dOptions}}. The optional parameters of the operation.
 
-    **Returns:** an {{MLOperand}}. The output 4-D tensor that contains the convolution result. The output shape is interpreted according to *options*.{{MLConv2dOptions/inputLayout}}. More specifically, the spatial dimensions or the sizes of the last two dimensions of the output tensor for the {{MLInputOperandLayout/"nchw"}} input layout can be calculated as follows:
+    **Returns:** an {{MLOperand}}. The output 4-D tensor that contains the convolution result. The output shape is interpreted according to {{MLConv2dOptions/inputLayout}}. More specifically, the spatial dimensions or the sizes of the last two dimensions of the output tensor for the {{MLInputOperandLayout/"nchw"}} input layout can be calculated as follows:
 
     `outputSize = 1 + (inputSize - (filterSize - 1) * dilation - 1 + beginningPadding + endingPadding) / stride`
 </div>
@@ -2495,7 +2495,7 @@ partial dictionary MLOpSupportLimits {
 </dl>
 
 <div class="note">
-    A *depthwise* conv2d operation is a variant of grouped convolution, used in models like the MobileNet, where the *options*.{{MLConv2dOptions/groups}} = inputChannels = outputChannels and the shape of filter tensor is *[options.groups, 1, height, width]*
+    A *depthwise* conv2d operation is a variant of grouped convolution, used in models like the MobileNet, where the {{MLConv2dOptions/groups}} = inputChannels = outputChannels and the shape of filter tensor is *[options.groups, 1, height, width]*
     for {{MLConv2dFilterOperandLayout/"oihw"}} layout, *[height, width, 1, options.groups]* for {{MLConv2dFilterOperandLayout/"hwio"}} layout, *[options.groups, height, width, 1]* for {{MLConv2dFilterOperandLayout/"ohwi"}} layout and *[1, height, width, options.groups]* for {{MLConv2dFilterOperandLayout/"ihwo"}} layout.
 </div>
 
@@ -2636,7 +2636,7 @@ partial dictionary MLOpSupportLimits {
     : <dfn>outputPadding</dfn>
     ::
         A list of length 2.
-        Specifies the padding values applied to each spatial dimension of the output tensor. The explicit padding values are needed to disambiguate the output tensor shape for transposed convolution when the value of the *options*.{{MLConvTranspose2dOptions/strides}} is greater than 1.
+        Specifies the padding values applied to each spatial dimension of the output tensor. The explicit padding values are needed to disambiguate the output tensor shape for transposed convolution when the value of the {{MLConvTranspose2dOptions/strides}} is greater than 1.
 
         Note that these values are only used to disambiguate output shape when needed; it does not necessarily cause any padding value to be written to the output tensor.
 
@@ -2678,12 +2678,12 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 4-D tensor. The logical shape
-            is interpreted according to the value of *options*.{{MLConvTranspose2dOptions/inputLayout}}.
+            is interpreted according to the value of {{MLConvTranspose2dOptions/inputLayout}}.
         - <dfn>filter</dfn>: an {{MLOperand}}. The filter 4-D tensor. The logical shape is
-            interpreted according to the value of *options*.{{MLConvTranspose2dOptions/filterLayout}} and {{MLConvTranspose2dOptions/groups}}.
+            interpreted according to the value of {{MLConvTranspose2dOptions/filterLayout}} and {{MLConvTranspose2dOptions/groups}}.
         - <dfn>options</dfn>: an optional {{MLConvTranspose2dOptions}}.
 
-    **Returns:** an {{MLOperand}}. The output 4-D tensor that contains the transposed convolution result. The output shape is interpreted according to *options*.{{MLConvTranspose2dOptions/inputLayout}}. More specifically, unless *options*.{{MLConvTranspose2dOptions/outputSizes}} is explicitly specified, *options*.{{MLConvTranspose2dOptions/outputPadding}} is needed to compute the spatial dimension values of the output tensor as follows:
+    **Returns:** an {{MLOperand}}. The output 4-D tensor that contains the transposed convolution result. The output shape is interpreted according to {{MLConvTranspose2dOptions/inputLayout}}. More specifically, unless {{MLConvTranspose2dOptions/outputSizes}} is explicitly specified, {{MLConvTranspose2dOptions/outputPadding}} is needed to compute the spatial dimension values of the output tensor as follows:
 
     `outputSize = (inputSize - 1) * stride + (filterSize - 1) * dilation + 1 - beginningPadding - endingPadding + outputPadding`
 </div>
@@ -3587,7 +3587,7 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/gather(input, indices, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input N-D tensor from which the values are gathered.
-        - <dfn>indices</dfn>: an {{MLOperand}}. The indices N-D tensor of the input values to gather. The values must be of type {{MLOperandDataType/"int32"}}, {{MLOperandDataType/"uint32"}} or {{MLOperandDataType/"int64"}}, and must be in the range -N (inclusive) to N (exclusive) where N is the size of the input dimension indexed by *options*.{{MLGatherOptions/axis}}, and a negative index means indexing from the end of the dimension.
+        - <dfn>indices</dfn>: an {{MLOperand}}. The indices N-D tensor of the input values to gather. The values must be of type {{MLOperandDataType/"int32"}}, {{MLOperandDataType/"uint32"}} or {{MLOperandDataType/"int64"}}, and must be in the range -N (inclusive) to N (exclusive) where N is the size of the input dimension indexed by {{MLGatherOptions/axis}}, and a negative index means indexing from the end of the dimension.
         - <dfn>options</dfn>: an optional {{MLGatherOptions}}. The optional parameters of the operation.
 
     **Returns:** an {{MLOperand}}. The output N-D tensor of [=MLOperand/rank=] equal to the [=MLOperand/rank=] of *input* + the [=MLOperand/rank=] of *indices* - 1.
@@ -4075,13 +4075,13 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 3-D tensor of shape *[steps, batchSize, inputSize]*.
-        - <dfn>weight</dfn>: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLGruOptions/layout}}.
-        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLGruOptions/layout}}.
+        - <dfn>weight</dfn>: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to {{MLGruOptions/layout}}.
+        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to {{MLGruOptions/layout}}.
         - <dfn>steps</dfn>: an {{unsigned long}} scalar. The number of time steps in the recurrent network. The value must be greater than 0.
         - <dfn>hiddenSize</dfn>: an {{unsigned long}} scalar. The value of the third dimension of the cell output tensor shape. It indicates the number of features in the hidden state.
         - <dfn>options</dfn>: an optional {{MLGruOptions}}. The optional parameters of the operation.
 
-    **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the cell output from the last time step of the network. Additionally, if *options*.{{MLGruOptions/returnSequence}} is set to true, the second element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every cell outputs from each time step in the temporal sequence.
+    **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the cell output from the last time step of the network. Additionally, if {{MLGruOptions/returnSequence}} is set to true, the second element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every cell outputs from each time step in the temporal sequence.
 </div>
 
 <table id=constraints-gru class='data' link-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options)">
@@ -4379,8 +4379,8 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 2-D tensor of shape *[batchSize, inputSize]*.
-        - <dfn>weight</dfn>: an {{MLOperand}}. The 2-D input weight tensor of shape *[3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLGruCellOptions/layout}}.
-        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLGruCellOptions/layout}}.
+        - <dfn>weight</dfn>: an {{MLOperand}}. The 2-D input weight tensor of shape *[3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to {{MLGruCellOptions/layout}}.
+        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to {{MLGruCellOptions/layout}}.
         - <dfn>hiddenState</dfn>: an {{MLOperand}}. The 2-D input hidden state tensor of shape *[batchSize, hiddenSize]*.
         - <dfn>hiddenSize</dfn>: an {{unsigned long}} scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.
         - <dfn>options</dfn>: an optional {{MLGruCellOptions}}. The optional parameters of the operation.
@@ -5376,13 +5376,13 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 3-D tensor of shape *[steps, batchSize, inputSize]*.
-        - <dfn>weight</dfn>: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLLstmOptions/layout}}.
-        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLLstmOptions/layout}}.
+        - <dfn>weight</dfn>: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to {{MLLstmOptions/layout}}.
+        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to {{MLLstmOptions/layout}}.
         - <dfn>steps</dfn>: an {{unsigned long}} scalar. The number of time steps in the recurrent network. The value must be greater than 0.
         - <dfn>hiddenSize</dfn>: an {{unsigned long}} scalar. The value of the third dimension of the cell output tensor shape. It indicates the number of features in the hidden state.
         - <dfn>options</dfn>: an optional {{MLLstmOptions}}. The optional parameters of the operation.
 
-    **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output cell state from the last time step of the network. Additionally, if *options*.{{MLLstmOptions/returnSequence}} is set to true, the third element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every output from each time step in the temporal sequence.
+    **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output cell state from the last time step of the network. Additionally, if {{MLLstmOptions/returnSequence}} is set to true, the third element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every output from each time step in the temporal sequence.
 </div>
 
 <table id=constraints-lstm class='data' link-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options)">
@@ -5735,8 +5735,8 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 2-D tensor of shape *[batchSize, inputSize]*.
-        - <dfn>weight</dfn>: an {{MLOperand}}. The 2-D input weight tensor of shape *[4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLLstmCellOptions/layout}}.
-        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLLstmCellOptions/layout}}.
+        - <dfn>weight</dfn>: an {{MLOperand}}. The 2-D input weight tensor of shape *[4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to {{MLLstmCellOptions/layout}}.
+        - <dfn>recurrentWeight</dfn>: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to {{MLLstmCellOptions/layout}}.
         - <dfn>hiddenState</dfn>: an {{MLOperand}}. The 2-D input hidden state tensor of shape *[batchSize, hiddenSize]*.
         - <dfn>cellState</dfn>: an {{MLOperand}}. The 2-D input cell state tensor of shape *[batchSize, hiddenSize]*.
         - <dfn>hiddenSize</dfn>: an {{unsigned long}} scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.
@@ -6346,16 +6346,16 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/averagePool2d(input, options), MLGraphBuilder/l2Pool2d(input, options), MLGraphBuilder/maxPool2d(input, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input 4-D tensor. The logical shape
-            is interpreted according to the value of *options*.{{MLPool2dOptions/layout}}.
+            is interpreted according to the value of {{MLPool2dOptions/layout}}.
         - <dfn>options</dfn>: an optional {{MLPool2dOptions}}. The optional parameters of the operation.
 
     **Returns:** an {{MLOperand}}. The output 4-D tensor that contains the
     result of the reduction. The logical shape is interpreted according to the
-    value of {{MLPool2dOptions/layout}}. More specifically, if the *options*.{{MLPool2dOptions/roundingType}} is {{MLRoundingType/"floor"}}, the spatial dimensions of the output tensor can be calculated as follows:
+    value of {{MLPool2dOptions/layout}}. More specifically, if the {{MLPool2dOptions/roundingType}} is {{MLRoundingType/"floor"}}, the spatial dimensions of the output tensor can be calculated as follows:
 
     `output size = floor(1 + (input size - filter size + beginning padding + ending padding) / stride)`
 
-    or if *options*.{{MLPool2dOptions/roundingType}} is {{MLRoundingType/"ceil"}}:
+    or if {{MLPool2dOptions/roundingType}} is {{MLRoundingType/"ceil"}}:
 
     `output size = ceil(1 + (input size - filter size + beginning padding + ending padding) / stride)`
 </div>
@@ -7582,7 +7582,7 @@ partial dictionary MLOpSupportLimits {
 <div dfn-for="MLGraphBuilder/split(input, splits, options)" dfn-type=argument>
     **Arguments:**
         - <dfn>input</dfn>: an {{MLOperand}}. The input tensor.
-        - <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>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 {{MLSplitOptions/axis}}. If a [=sequence=]<{{unsigned long}}>, it specifies the sizes of each output tensor along the {{MLSplitOptions/axis}}. The sum of sizes must equal to the dimension size of *input* along {{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*].