Versioned name: SquaredDifference-1
Short description: SquaredDifference performs element-wise subtraction operation with two given tensors applying multi-directional broadcast rules, after that each result of the subtraction is squared.
OpenVINO description: This OP is as same as OpenVINO OP
Description: specifies rules used for auto-broadcasting of input tensors.
Range of values:
none - no auto-broadcasting is allowed, all input shapes should match
numpy - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in ONNX docs.
Default value: numpy
input_1- the first input tensor. Required.
input_2- the second input tensor. Required.
output- the output tensor of SquaredDifference operation. Required.
T: f32, f16, bf16.
Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.
Before performing arithmetic operation, input_1 and input_2 are broadcasted
if their shapes are different and
auto_broadcast attributes is
none. Broadcasting is performed according to
After broadcasting SquaredDifference does the following with the input tensors: