// CHECK-NEXT:     } ]
// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }
// CHECK-EMPTY:

^bb0(%arg0: tensor<4 x f32>, %arg1: tensor<4 x f32>, %arg2: tensor<4 x f32>, %arg3: tensor<4 x f32>):
  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32> loc("Const")

// CHECK-NEXT:  metadata: [ {
// CHECK-NEXT:  name: "min_runtime_version",
// CHECK-NEXT:  buffer: 6
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul0")

  %1 = "tfl.batch_matmul"(%0, %arg0) {adj_x = false, adj_y = false} : (tensor<1x1x16384xi32>, tensor<1x16384x257xi32>) -> tensor<1x1x257xi32>
  func.return %1 : tensor<1x1x257xi32>
  // CHECK: %[[CONST_DIM:.*]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[RED:.*]] = "tfl.sum"(%arg0, %[[CONST_DIM]]) <{keep_dims = true}> : (tensor<1x16384x257xi32>, tensor<1xi32>) -> tensor<1x1x257xi32>
}

  %cst0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")
  %cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")

  %cst0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")
  %cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")

  func.return %1: tensor<1x5xf32>
}

// -----

func.func @Int64SliceBeginSize(%arg0: tensor<4x128x32xf32>) -> tensor<1x128x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi64>} : () -> tensor<3xi64>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 128, 32]> : tensor<3xi64>} : () -> tensor<3xi64>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x128x32xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x128x32xf32>

// CHECK-NEXT:  metadata: [ {
// CHECK-NEXT:  name: "min_runtime_version",
// CHECK-NEXT:  buffer: 6
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")

// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<[1, 401408]> : tensor<2xi32>} : () -> tensor<2xi32> loc("Const")
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

// CHECK-NEXT:   }, {
// CHECK-NEXT:   name: "model_control_dependencies",
// CHECK-NEXT:   buffer: 7
// CHECK-NEXT:   } ],
// CHECK-NEXT:   signature_defs: [ ]
// CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")

  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>> {