failed(AddFloatAttr(func, attrs, "nms_iou_threshold", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "y_scale", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "x_scale", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "h_scale", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "w_scale", &fbb)))
    return failure();
  auto use_regular_nms =
      mlir::dyn_cast_or_null<BoolAttr>(attrs.get("use_regular_nms"));

    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "

  %0 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>

        in_x);
    auto out_h_f32 = rewriter.create<CastOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getF32Type()),
        out_x);

    Value x_scale = rewriter.create<DivOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getF32Type()),
        in_x_f32, out_h_f32);

    Value x_range = rewriter.create<RangeOp>(
        loc,

// CHECK-DAG: {{\[\[\[}}-87, -42]

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0, %[[in_scale]], %[[in_zp]])
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]], %[[w_quant]], %[[b_quant]],
// CHECK-SAME: %[[in_scale]], %[[in_zp]], %[[w_scale]], %[[w_zp]],
// CHECK-SAME: %[[b_scale]], %[[w_zp]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_fn_0

// TF-DAG: %[[arg_quantized:.*]] = "tf.PartitionedCall"(%arg0, %[[in_scale]], %[[in_out_zp]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}>

    const total = pos + neg;
    const xscale = (width-2*PADDING) / total; // Converts from profile value to X pixels
    const x = PADDING;
    const y = 0;

    displayList.length = 0;
    renderStacks(0, xscale, x, y, places, +1);  // Callees
    renderStacks(0, xscale, x, y-ROW, places, -1);  // Callers (ROW left for separator)
    display(xscale, pos, neg, displayList);
  }

	Status                           *v1beta2.ScaleStatus `json:"status,omitempty"`
}

// ScaleApplyConfiguration constructs an declarative configuration of the Scale type for use with
// apply.
func Scale() *ScaleApplyConfiguration {
	b := &ScaleApplyConfiguration{}
	b.WithKind("Scale")
	b.WithAPIVersion("apps/v1beta2")
	return b
}

// WithKind sets the Kind field in the declarative configuration to the given value

}

// ScaleApplyConfiguration constructs an declarative configuration of the Scale type for use with
// apply.
func Scale() *ScaleApplyConfiguration {
	b := &ScaleApplyConfiguration{}
	b.WithKind("Scale")
	b.WithAPIVersion("autoscaling/v1")
	return b
}

// WithKind sets the Kind field in the declarative configuration to the given value

// CHECK-DAG: %[[q_w:.*]] = "tf.Const"() <{value = #tf_type<tensor_proto : "0x746674
// CHECK-DAG: %[[w_scale:.*]] = "tf.Const"() <{value = dense<0.0157480314> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[w_zp:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>