func.return %2 : tensor<2x2xf32>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_const"(){{.*}}dense<-1.000000e-01> : tensor<2x2xf32>
// CHECK:  return %[[cst]] : tensor<2x2xf32>
}

// CHECK-LABEL: DequantizeAndQuantize
func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

    }

    public void writeVersionConstraint(Encoder encoder, VersionConstraint cst) throws IOException {
        encoder.writeString(cst.getRequiredVersion());
        encoder.writeString(cst.getPreferredVersion());
        encoder.writeString(cst.getStrictVersion());
        List<String> rejectedVersions = cst.getRejectedVersions();
        encoder.writeSmallInt(rejectedVersions.size());

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>
  %0:2 = "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>, tensor<40x40xf32>)

// make it easier to create Relu1 matching patterns.
def SwapMaximumOperands : Pat<
  (TF_MaximumOp (TF_ConstOp:$cst $cst_val), $input),
  (TF_MaximumOp $input, $cst)>;

def SwapMinimumOperands : Pat<
  (TF_MinimumOp (TF_ConstOp:$cst $cst_val), $input),
  (TF_MinimumOp $input, $cst)>;

// Relu1 activation is represented as a couple of Max and Min ops, The following

  %cst = arith.constant dense<[1, 2]> : tensor<2xi64>
  %0 = "tf.Reshape"(%arg0, %cst) : (tensor<*xf32>, tensor<2xi64>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>

// CHECK-LABEL: dynamicReshapeI64Fold
// CHECK:  %[[cst:.*]] = arith.constant dense<[1, 2]> : tensor<2xi32>
// CHECK-NEXT:  %[[reshape:.*]] = "tfl.reshape"(%arg0, %[[cst]]) : (tensor<*xf32>, tensor<2xi32>) -> tensor<1x2xf32>

  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>

  %cst = arith.constant dense<1.000000e+00> : tensor<256x256xf32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>

// CHECK-NEXT: %[[V1:.*]] = "tf.PartitionedCall"(%[[ARG0]], %[[CST]], %[[CST_0]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}> : (tensor<1x3x4x3xf32>, tensor<f32>, tensor<i32>) -> tensor<1x3x4x3xi8>

module {
  func.func @main() -> tensor<*xi32> {
    %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<*xi32>>>
    %cst = arith.constant dense<2> : tensor<4xi32>
    "tf.AssignAddVariableOp"(%0, %cst) {} : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<4xi32>) -> ()
    %1 = "tf.ReadVariableOp"(%0) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<*xi32>