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@@ -7134,7 +7134,7 @@ partial dictionary MLOpSupportLimits {
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::
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The interpolation algorithm used to fill the output tensor values.
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Both algorithms start with these inputs, computed for each spatial axis:
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Both algorithms start with these inputs, computed for each spatial axis (based on {{MLResample2dOptions/axes}}), where `inputSize` is given by the {{MLGraphBuilder/resample2d(input, options)/input}} tensor's [=MLTensor/shape=], `outputSize` is given by {{MLResample2dOptions/sizes}} or {{MLResample2dOptions/scales}}, and `outputCoordinate` identifies the element in the output tensor being computed.
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```
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scale = outputSize / inputSize
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unclampedCoordinate = (outputCoordinate + 0.5) / scale - 0.5
@@ -7162,13 +7162,13 @@ partial dictionary MLOpSupportLimits {
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y1 = ceil(inputCoordinate.y)
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vx0y0 = input tensor value at (x0, y0)
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vx1y0 = input tensor value at (x1, y0)
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v01 = input tensor value at (x0, y1)
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vx0y1 = input tensor value at (x0, y1)
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vx1y1 = input tensor value at (x1, y1)
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tx = inputCoordinate.x - x0
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ty = inputCoordinate.y - y0
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vy0 = vx0y0 * (1 - tx) + vx1y0 * tx;
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vy1 = vx0y1 * (1 - tx) + vx1y1 * tx;
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vy0 = vx0y0 * (1 - tx) + vx1y0 * tx
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vy1 = vx0y1 * (1 - tx) + vx1y1 * tx
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output tensor value = vy0 * (1 - ty) + vy1 * ty
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```
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</dl>
@@ -7287,7 +7287,7 @@ partial dictionary MLOpSupportLimits {
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[ 12 12.25 12.75 13.25 13.75 14.25 14.75 15 ]
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```
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This has the convenient properties that the sampling is evenly distributed and the corner values are aligned when up-sampling.
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This has the convenient properties that the sampling is evenly distributed, symmetric, robust to image mirroring, and the corner values are aligned (when up-sampling).
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</div>
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### reshape ### {#api-mlgraphbuilder-reshape-method}

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