// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x3xf32>

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

module {
  func.func @simpleTest(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.interface_name = "func_1"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

// expected-error@-1 {{'tf_executor.Switch' op operand #1 must be tensor of 1-bit signless integer values}}
    tf_executor.fetch %true : tensor<*xf32>
  }
  func.return %result : tensor<*xf32>
}

// -----

// Check that a switch result type matches the input type.
func.func @invalid_switch(%arg0: tensor<*xf32>, %arg1: tensor<i1>) -> tensor<*xf32> {

    %cst = "tf.Const"() <{value = dense<true> : tensor<i1>}> {device = ""} : () -> tensor<i1>
    %cst_0 = "tf.Const"() <{value = dense<[0.117216609, 0.933735609, 0.0728900209]> : tensor<3xf32>}> {device = ""} : () -> tensor<3xf32>

  // expected-remark@above {{Sinks: {}}}
}

// -----

func.func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  // expected-remark@above {{ID: 2}}
  %sum = "tf.Add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  // expected-remark@above {{ID: 0}}
  func.return %sum : tensor<1xf32>
  // expected-remark@above {{ID: 1}}
  // expected-remark@above {{Sinks: {}}}
}

  %cst_1 = arith.constant dense<0> : tensor<4xi32>
  %cst_2 = arith.constant dense<0.000000e+00> : tensor<1xf32>
  %0 = "tfl.quantize"(%cst_2) {qtype = tensor<1x!quant.uniform<i32:f32, 1.0>>, volatile} : (tensor<1xf32>) -> tensor<1x!quant.uniform<i32:f32, 1.0>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x!quant.uniform<i32:f32, 1.0>>) -> tensor<1xf32>
  %cst_3 = arith.constant dense<[[[[1.0]], [[1.0]], [[1.0]]]]> : tensor<1x3x1x1xf32>

  %0 = "tfr.cast"(%arg0) : (tensor<1x3x!quant.uniform<i8:f32:1, {0.1:1, 0.2:2, 0.3:3}>>) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%0) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %1 = "tfr.cast"(%scale) : (!tfr.tensor) -> tensor<3xf32>
  %2 = "tfr.cast"(%zp) : (!tfr.tensor) -> tensor<3xi32>
  func.return %1, %2 : tensor<3xf32>, tensor<3xi32>

    %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
    %cst_0 = "tf.Const"() {value = dense<4.00000000e-1> : tensor<3xf32>} : () -> tensor<3xf32>
    %0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<?x2xf32>) -> tensor<?x2xf32>

  // CHECK: %[[VAL:.*]] = "tf.Const"() <{value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00]> : tensor<3xf32>}> : () -> tensor<3xf32>
  %value = "tf.Const"() {value = dense<[1.0, 2.0, 3.0]> : tensor<3xf32>} : () -> tensor<3xf32>
  // CHECK: %[[READ_VAR:.*]] = "tf.ReadVariableOp"(%[[VAR]])
  // CHECK: %[[UPDATE_SLICE:.*]] = "tf.Reshape"(%[[VAL]]
  // CHECK-SAME: -> tensor<1x3xf32>