Fixes the problem if scale is a tensor. scale[0] does not return a scalar, if scale is a multidimensional array (e.g. qonnx returns array shape 1x18)

Author: jurevreca12

hls4ml/model/optimizer/passes/quant_opt.py

@@ -167,8 +167,8 @@ def match(self, node):
             scale_unit_or_po2 = (scale == np.ones_like(scale)).all()
             if not scale_unit_or_po2 and _ALSO_MATCH_PO2:
                 # This optimization only works if all scales are the same
-                if np.all(scale[0] == scale):
-                    mantissa, _ = np.frexp(scale[0])
+                if np.all(scale.item(0) == scale):
+                    mantissa, _ = np.frexp(scale.item(0))
                     scale_unit_or_po2 = mantissa == 0.5
 
             is_match = scale_unit_or_po2
@@ -187,7 +187,7 @@ def transform(self, model, node):
         integer = bitwidth
         scale = node.get_attr('scale')
         if _ALSO_MATCH_PO2 and not (scale == np.ones_like(scale)).all():
-            _, exp = np.frexp(scale[0])  # know that np.all(scale[0] == scale) must be true
+            _, exp = np.frexp(scale.item(0))  # know that np.all(scale.item(0) == scale) must be true
             integer = bitwidth + exp - 1
 
         precision, quantizer = _calculate_precision_quantizer(bitwidth, integer, signed, narrow, rounding_mode)