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@ -58,7 +58,8 @@ Limited Elementwise {name} Operator.
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The equation is:
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{equation}
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.. math::
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{equation}
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X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
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or equal to the dimensions of X.
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@ -71,15 +72,16 @@ For case 2:
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Y will be broadcasted to match the shape of X and axis should be
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the starting dimension index for broadcasting Y onto X.
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example:
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shape(X) = (2, 3, 4, 5), shape(Y) = (,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
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shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
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shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
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For example
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.. code-block:: python
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Both the input X and Y can carry the LoD (Level of Details) information,
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or not. But the output only shares the LoD information with input X.
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shape(X) = (2, 3, 4, 5), shape(Y) = (,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
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shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
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shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
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shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
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Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.
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)DOC";
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