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UpdatedOctober 29, 2025

lsNaN

Description

Returns which elements of the input are NaN.

 

Input parameters

 

specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
X (heterogeneous) – T1 : object, input tensor.

Β Parameters :Β cluster,

Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β€œ1”.

Β name (optional) :Β string, name of the node.

Output parameters

 

Β Y (heterogeneous) – T2 : object, output tensor.

Type Constraints

T1 in (tensor(bfloat16),Β tensor(double),Β tensor(float),Β tensor(float16),Β tensor(float8e4m3fn),Β tensor(float8e4m3fnuz),
tensor(float8e5m2),Β tensor(float8e5m2fnuz)) : Constrain input types to float tensors.

T2 in (tensor(bool)) : Constrain output types to boolean tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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