DynamicQuantizeMatMul

 Graphs in : cluster, ONNX model architecture.

A (heterogeneous) – T1 : object, N-dimensional matrix A.
B (heterogeneous) – T2 : object, N-dimensional matrix B.
b_scale (heterogeneous) – T1 : object, scale of quantized input ‘B’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of columns of input ‘B’.
b_zero_point (optional, heterogeneous) – T2 : object, zero point tensor for input ‘B’. It’s optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of columns of input ‘B’.
bias (optional, heterogeneous) – T1 : object, 1D input tensor, whose dimension is same as B’s last dimension.

 Parameters : cluster,

 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : 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 “1”.

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