// CHECK-SAME:        window_strides = dense<[1, 3, 4, 1]>
// CHECK:           ^bb0(%[[ARG1:.*]]: tensor<f32>, %[[ARG2:.*]]: tensor<f32>):
// CHECK:             %[[SUM1:.*]] = mhlo.add %[[ARG1]], %[[ARG2]] : tensor<f32>
// CHECK:             mhlo.return %[[SUM1]] : tensor<f32>
// CHECK:           })
// CHECK-SAME:        -> tensor<2x4x6x7xf32>
// CHECK:           %[[ONES:.*]] = mhlo.constant dense<1.000000e+00> : tensor<2x12x21x7xf32>

	{VPMSUMB, 0xfc0007ff00000000, 0x1000040800000000, 0x0, // Vector Polynomial Multiply-Sum Byte VX-form (vpmsumb VRT,VRA,VRB)
		[6]*argField{ap_VecReg_6_10, ap_VecReg_11_15, ap_VecReg_16_20}},
	{VPMSUMD, 0xfc0007ff00000000, 0x100004c800000000, 0x0, // Vector Polynomial Multiply-Sum Doubleword VX-form (vpmsumd VRT,VRA,VRB)
		[6]*argField{ap_VecReg_6_10, ap_VecReg_11_15, ap_VecReg_16_20}},

  }
};

// Converts Mean op to HLO Reduce op.
//
//   %init = arith.constant dense<...> : tensor<T>
//   %sum = "mhlo.reduce"(%inp, %init) ["mhlo.add"]
//               {dimensions = ...}
//   %divisor = arith.constant dense<...> : tensor<T>
//   %mean = "mhlo.divide"(%sum, %divisor)
class ConvertMeanOp
    : public GenericConvertReductionOp<ConvertMeanOp, TF::MeanOp, AddOp> {
 public:

  // most preferred is the one with the greatest sum of weights, i.e.
  // for each node that meets all of the scheduling requirements (resource
  // request, requiredDuringScheduling affinity expressions, etc.),
  // compute a sum by iterating through the elements of this field and adding
  // "weight" to the sum if the node matches the corresponding matchExpressions; the
  // node(s) with the highest sum are the most preferred.
  // +optional

}

	// most preferred is the one with the greatest sum of weights, i.e.
	// for each node that meets all of the scheduling requirements (resource
	// request, requiredDuringScheduling affinity expressions, etc.),
	// compute a sum by iterating through the elements of this field and adding
	// "weight" to the sum if the node has pods which matches the corresponding podAffinityTerm; the
	// node(s) with the highest sum are the most preferred.
	// +optional

  // most preferred is the one with the greatest sum of weights, i.e.
  // for each node that meets all of the scheduling requirements (resource
  // request, requiredDuringScheduling affinity expressions, etc.),
  // compute a sum by iterating through the elements of this field and adding
  // "weight" to the sum if the node matches the corresponding matchExpressions; the
  // node(s) with the highest sum are the most preferred.
  // +optional

	// most preferred is the one with the greatest sum of weights, i.e.
	// for each node that meets all of the scheduling requirements (resource
	// request, requiredDuringScheduling affinity expressions, etc.),
	// compute a sum by iterating through the elements of this field and adding
	// "weight" to the sum if the node has pods which matches the corresponding podAffinityTerm; the
	// node(s) with the highest sum are the most preferred.
	// +optional

	if f.conn != nil {
		f.conn.n -= n
	}
}

// add adds n bytes (positive or negative) to the flow control window.
// It returns false if the sum would exceed 2^31-1.
func (f *http2outflow) add(n int32) bool {
	sum := f.n + n
	if (sum > n) == (f.n > 0) {
		f.n = sum
		return true
	}
	return false
}

const http2frameHeaderLen = 9

var http2padZeros = make([]byte, 255) // zeros for padding

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>
// CHECK:           %[[VAL_3:.*]] = "tf.Sum"(%[[VAL_0]], %[[VAL_2]]) <{keep_dims = false}> : (tensor<1x256xf32>, tensor<1xi64>) -> tensor<1xf32>
// CHECK:           return %[[VAL_3]] : tensor<1xf32>
// CHECK:         }