%cst_perm = arith.constant dense<0> : tensor<1xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<{{\[}}1, 2, 3]> : tensor<3xi32>
  // CHECK: return %[[CST]]
  %0 = "tfl.transpose"(%cst, %cst_perm) : (tensor<3xi32>, tensor<1xi32>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// CHECK-LABEL: @transpose_dynamic
func.func @transpose_dynamic() -> tensor<?xi32> {
  %cst = arith.constant dense<[1, 2, 3]> : tensor<3xi32>

// CHECK-NEXT: %[[s3:.*]] = "tfl.shape"(%arg1) : (tensor<?x?x4x!quant.uniform<u8:f32, 1.000000e+00>>) -> tensor<3xi32>
// CHECK-NEXT: %[[s2]], %[[s3]] : tensor<?xi32>, tensor<3xi32>
}

// CHECK-LABEL: QuantizeMultipleUsers
func.func @QuantizeMultipleUsers(%arg1: tensor<?x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1xi32>,tensor<1xi32>) {

  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = func.call @simple_test(%arg0, %0, %1) {tac.interface_name = "func1"} : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2 : tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @simple_test

#loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \22tf.Reshape\22(%arg0, %arg1) {_xla_outside_compilation = \220\22} : (tensor<3360x?xi32>, tensor<3xi32>) -> tensor<3x1120x?xi32> loc(#loc9)\0A    %1:3 = ...

  // CHECK-DAG: [[OFFSET_2:%.*]] = "tf.Const{{.*}} dense<[0, 5, 0]> : tensor<3xi32>

  %offset:3 = "tf.ConcatOffset"(%concat_dim, %shape0, %shape1, %shape2) : (tensor<i32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> (tensor<3xi32>, tensor<3xi32>, tensor<3xi32>)

  // CHECK: return [[OFFSET_0]], [[OFFSET_1]], [[OFFSET_2]]
  func.return %offset#0, %offset#1, %offset#2: tensor<3xi32>, tensor<3xi32>, tensor<3xi32>
}

func.func @padSliceTo4D(%arg0: tensor<4x384x32xf32>) -> tensor<1x384x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2 : tensor<1x384x32xf32>
}

// CHECK-DAG:       %[[VAL_5:.*]] = arith.constant dense<[1, 384, 128]> : tensor<3xi32>
// CHECK-DAG:       %[[VAL_6:.*]] = "tfl.pseudo_const"(){{.*}}dense<[1, 1, 384, 512]> : tensor<4xi32>
// CHECK-DAG:       %[[VAL_7:.*]] = "tfl.pseudo_const"(){{.*}}dense<[128, 1, 1, 512]> : tensor<4xi32>
// CHECK-DAG:       %[[VAL_8:.*]] = "tfl.pseudo_const"(){{.*}}dense<[384, 128]> : tensor<2xi32>

    // CHECK: [[TENSOR:%.+]] = "tf.TensorScatterAdd"([[READ]], [[EXPAND]], [[UPDATE]]) : (tensor<*xi32>, tensor<?x1xi32>, tensor<?x?x?xi32>) -> tensor<*xi32>
    // CHECK: "tf.AssignVariableOp"([[VAR]], [[TENSOR]])
    "tf.ResourceScatterAdd"(%resource, %indices, %updates) : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<?xi32>, tensor<?x?x?xi32>) -> ()

    tf_device.return

  %1 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = "tfl.pseudo_const"() {value = dense<[0, 2, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
  %3 = "tfl.pseudo_const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>

  %4 = "tfl.var_handle"() {container = "", shared_name = "read_assign/states0"} : () -> tensor<!tf_type.resource>

func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

// CHECK-LABEL: testReshapeShapeInferenceUnknownDim