// 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>

      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"));

// 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

    "'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>

// XLA-DAG: %[[conv1_dequantized:.*]] = "tf.PartitionedCall"(%[[conv0_quantized]], %[[w1_quantized]], %[[b1_quantized]], %[[mid_scale]], %[[in_out_zp]], %[[w1_scale]], %[[w_b_zp]], %[[b1_scale]], %[[w_b_zp]], %[[out_scale]],...

// CHECK-DAG: %[[w0_quantized:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}-42, 18
// CHECK-DAG: %[[in_scale:.*]] = "tf.Const"() <{value = dense<0.0039215642> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[in_out_zp:.*]] = "tf.Const"() <{value = dense<-128> : tensor<i32>}> : () -> tensor<i32>
// CHECK-DAG: %[[w0_scale:.*]] = "tf.Const"() <{value = dense<0.0150353173> : tensor<f32>}> : () -> tensor<f32>

	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

	return time.Duration(float64(ss) / seats * (float64(time.Second) / ssScale))
}

// String converts to a string.
// This is suitable for large as well as small values.
func (ss SeatSeconds) String() string {
	const div = SeatSeconds(ssScale)
	quo := ss / div
	rem := ss - quo*div
	return fmt.Sprintf("%d.%08dss", quo, rem)
}