**Arguments:**
- -
a: an {{MLOperand}}. The first input 2-D tensor with shape *[M, K]* if *aTranspose* is false, or *[K, M]* if *aTranspose* is true.
- -
b: an {{MLOperand}}. The second input 2-D tensor with shape *[K, N]* if *bTranspose* is false, or *[N, K]* if *bTranspose* is true.
+ -
a: an {{MLOperand}}. The first input 2-D tensor with shape *[M, K]* if {{MLGemmOptions/aTranspose}} is false, or *[K, M]* if {{MLGemmOptions/aTranspose}} is true.
+ -
b: an {{MLOperand}}. The second input 2-D tensor with shape *[K, N]* if {{MLGemmOptions/bTranspose}} is false, or *[N, K]* if {{MLGemmOptions/bTranspose}} is true.
-
options: an optional {{MLGemmOptions}}. The optional parameters of the operation.
**Returns:** an {{MLOperand}}. The output 2-D tensor of shape *[M, N]* that contains the calculated product of all the inputs.
@@ -4979,7 +4979,7 @@ partial dictionary MLOpSupportLimits {
:
axes
::
- The indices to the input dimensions to reduce. When this member is not present, it is treated as if all dimensions except the first were given (e.g. for a 4-D input tensor, axes = [1,2,3]). That is, the reduction for the mean and variance values are calculated across all the input features for each independent batch. If empty, no dimensions are reduced.
+ The indices to the input dimensions to reduce. When this member is not present, it is treated as if all dimensions except the first were given (e.g. for a 4-D input tensor, {{MLLayerNormalizationOptions/axes}} = [1,2,3]). That is, the reduction for the mean and variance values are calculated across all the input features for each independent batch. If empty, no dimensions are reduced.
:
epsilon
::
A small value to prevent computational error due to divide-by-zero.
@@ -6346,16 +6346,16 @@ partial dictionary MLOpSupportLimits {
**Arguments:**
- input: an {{MLOperand}}. The input 4-D tensor. The logical shape
- is interpreted according to the value of *options.layout*.
+ is interpreted according to the value of *options*.{{MLPool2dOptions/layout}}.
- options: 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 *layout*. More specifically, if the *options.roundingType* is {{MLRoundingType/"floor"}}, the spatial dimensions of the output tensor can be calculated as follows:
+ 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:
`output size = floor(1 + (input size - filter size + beginning padding + ending padding) / stride)`
- or if *options.roundingType* is {{MLRoundingType/"ceil"}}:
+ or if *options*.{{MLPool2dOptions/roundingType}} is {{MLRoundingType/"ceil"}}:
`output size = ceil(1 + (input size - filter size + beginning padding + ending padding) / stride)`
@@ -6994,7 +6994,7 @@ partial dictionary MLOpSupportLimits {
:
sizes
::
A list of length 2.
- Specifies the target sizes for each input dimension from {{MLResample2dOptions/axes}} : *[sizeForFirstAxis, sizeForSecondAxis]*. When the target sizes are specified, {{MLResample2dOptions/scales}} is ignored, since the scaling factor values are derived from the target sizes of the input.
+ Specifies the target sizes for each input dimension from {{MLResample2dOptions/axes}}: *[sizeForFirstAxis, sizeForSecondAxis]*. When {{MLResample2dOptions/sizes}} is specified, {{MLResample2dOptions/scales}} is ignored, since the scaling factor values are derived from the target sizes of the input.
:
axes
::
@@ -7582,7 +7582,7 @@ partial dictionary MLOpSupportLimits {
**Arguments:**
- input: an {{MLOperand}}. The input tensor.
- - splits: 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.axis*. If a [=sequence=]<{{unsigned long}}>, it specifies the sizes of each output tensor along the *options.axis*. The sum of sizes must equal to the dimension size of *input* along *options.axis*.
+ - splits: 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}}.
- options: 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]*.