optimum/amd/brevitas/export.py [139:182]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        dq_weight = deq_linear.prevnodes[0]
        graph.add_initial(f"dq_weights_0_{node_count}", dq_weight.value.transpose())
        graph.add_initial(f"dq_weights_1_{node_count}", deq_linear.prevnodes[1].value)
        graph.add_initial(f"dq_weights_2_{node_count}", deq_linear.prevnodes[2].value)

        matmul = graph.nodemap[found_pattern[-1]]
        for name in found_pattern:
            if "DynamicQuantizeLinear" in name:
                continue
            graph.remove_node(name)

        graph.remove_node(deq_linear.prevnodes[0].name)
        if deq_linear.prevnodes[1].name in graph.nodemap:
            graph.remove_node(deq_linear.prevnodes[1].name)
        if deq_linear.prevnodes[2].name in graph.nodemap:
            graph.remove_node(deq_linear.prevnodes[2].name)

        graph = create_nodes(
            graph,
            "MatMulInteger",
            f"matmul_integer_{node_count}",
            [dyn_q.output[0], f"dq_weights_0_{node_count}", dyn_q.output[2], f"dq_weights_2_{node_count}"],
            [f"matmul_integer_{node_count}"],
        )
        graph = create_nodes(
            graph,
            "Cast",
            f"cast_{node_count}",
            [f"matmul_integer_{node_count}"],
            [f"cast_{node_count}"],
            to=ONNX_FLOAT32_IDENTIFIER,
        )
        graph = create_nodes(
            graph,
            "Mul",
            f"mulscales_{node_count}",
            [dyn_q.output[1], f"dq_weights_1_{node_count}"],
            [f"mulscales_{node_count}"],
        )
        graph = create_nodes(
            graph,
            "Mul",
            f"mulvalues_{node_count}",
            [f"mulscales_{node_count}", f"cast_{node_count}"],
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



optimum/amd/brevitas/export.py [196:238]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        dq_weight = deq_linear.prevnodes[0]
        graph.add_initial(f"dq_weights_0_{node_count}", dq_weight.value.transpose())
        graph.add_initial(f"dq_weights_1_{node_count}", deq_linear.prevnodes[1].value)
        graph.add_initial(f"dq_weights_2_{node_count}", deq_linear.prevnodes[2].value)

        matmul = graph.nodemap[found_pattern[-1]]
        for name in found_pattern:
            if "DynamicQuantizeLinear" in name:
                continue
            graph.remove_node(name)
        graph.remove_node(deq_linear.prevnodes[0].name)
        if deq_linear.prevnodes[1].name in graph.nodemap:
            graph.remove_node(deq_linear.prevnodes[1].name)
        if deq_linear.prevnodes[2].name in graph.nodemap:
            graph.remove_node(deq_linear.prevnodes[2].name)

        graph = create_nodes(
            graph,
            "MatMulInteger",
            f"matmul_integer_{node_count}",
            [dyn_q.output[0], f"dq_weights_0_{node_count}", dyn_q.output[2], f"dq_weights_2_{node_count}"],
            [f"matmul_integer_{node_count}"],
        )
        graph = create_nodes(
            graph,
            "Cast",
            f"cast_{node_count}",
            [f"matmul_integer_{node_count}"],
            [f"cast_{node_count}"],
            to=ONNX_FLOAT32_IDENTIFIER,
        )
        graph = create_nodes(
            graph,
            "Mul",
            f"mulscales_{node_count}",
            [dyn_q.output[1], f"dq_weights_1_{node_count}"],
            [f"mulscales_{node_count}"],
        )
        graph = create_nodes(
            graph,
            "Mul",
            f"mulvalues_{node_count}",
            [f"mulscales_{node_count}", f"cast_{node_count}"],
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -