}
  return ConstantFoldOpIfPossible(value.getDefiningOp()).front();
}

// Matches convolution op with "NHWC" data format or matmul op with false adj_y.
// The list of supported ops in this function is:
// - Conv2DOp
// - Conv3DOp
// - DepthwiseConv2dNativeOp
// - MatMulOp
// - BatchMatMulV2Op
LogicalResult MatchSupportedAffineOp(Operation* op, Value& binding_output,

func.func @fold_identity_n_test(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> (tensor<2x2xf32>, tensor<2x2xf32>) {
  // CHECK: tf.MatMul
  %outputs = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  %outputs_0 = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>

        }

        const int x_row =
            matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1);
        const int x_col =
            !matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1);

        const int y_row =
            matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1);
        const int y_col =
            !matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1);

		done1 := make(chan struct{}, 1)
		go matmult(done1, A, B, C, i0, i1, j0, mj, k0, k1, threshold)
		matmult(nil, A, B, C, i0, i1, mj, j1, k0, k1, threshold)
		<-done1
	} else if dk >= threshold {
		// divide in two by "k" axis
		// deliberately not parallel because of data races
		mk := k0 + dk/2
		matmult(nil, A, B, C, i0, i1, j0, j1, k0, mk, threshold)
		matmult(nil, A, B, C, i0, i1, j0, j1, mk, k1, threshold)
	} else {

   (IsInt8ElementType $weight),
   (IsConstTensor $weight),
   (IsInt32ElementType $matmul),
   (HasStaticShapeConstraint $weight)],
  [], (addBenefit 10)>;

// Convert Matmul with hybrid inputs (f32 activation/int8 weight) to XlaDotV2
def ConvertTFMatMulToXLADotV2OpWeightOnly : Pat<
  (TF_MatMulOp:$matmul
    $input,
    (TF_MulOp (TF_CastOp (TF_IdentityOp $weight), $truncate1), $scale),

  input: "sequential/quant_dense/MatMul/kquant/FakeQuantWithMinMaxVars/ReadVariableOp"
  input: "sequential/quant_dense/MatMul/kquant/FakeQuantWithMinMaxVars/ReadVariableOp_1"
  attr {
    key: "narrow_range"
    value {
      b: false
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "sequential/quant_dense/MatMul/kquant/IdentityN"

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1);
  TFE_OpSetDevice(matmul, remote_device_name, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

# CHECK-DAG: %[[MUL1:.*]] = "tf.MatMul"(%[[ARG0]], %[[ARG1]])
# CHECK-DAG: %[[MUL2:.*]] = "tf.MatMul"(%[[ARG1]], %[[ARG0]])
# CHECK:  %[[IDENTITY:.*]]:2 = "tf.IdentityN"(%[[MUL1]], %[[MUL2]])
# CHECK: return %[[IDENTITY]]#1, %[[IDENTITY]]#0


def Test():

  x = tf.constant(1.0, shape=(5, 3))
  y = tf.constant(1.0, shape=(3, 5))

  s = tf.matmul(x, y)
  t = tf.matmul(y, x)
  [t, s] = array_ops.identity_n([t, s])

# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2

  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(ctx);

  TFE_Op* matmul = MatMulOp(ctx, h0_task0, h0_task0);
  TFE_OpSetDevice(matmul, remote_device_name, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);