%0 = "tfr.cast"(%arg0) : (tensor<*xf32>) -> !tfr.tensor
  %1 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<*xi32>
  %2 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<2xi32>
  func.return %1, %2 : tensor<*xi32>, tensor<2xi32>

// CHECK: %[[tf_cast_unranked:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<*xf32>) -> tensor<*xi32>

  %0 = "tfl.pseudo_const"() {value = dense<[1, 4, 384, 32]> : tensor<4xi32>} : () -> tensor<4xi32>
  // CHECK-NOT: tac.device tac.inference_type
  %1 = "tfl.pseudo_const"() {value = dense<[4, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  // CHECK: tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8"
  %2 = "tfl.reshape"(%arg0, %0) : (tensor<4x384x32x!quant.uniform<i8:f32, 0.2:-3>>, tensor<4xi32>) -> tensor<1x4x384x32x!quant.uniform<i8:f32, 0.2:-3>>

    tf_executor.fetch %value : tensor<!tf_type.variant<tensor<8xf32>>>
  }
  func.return %result : tensor<!tf_type.variant<tensor<8xf32>>>
}

// CHECK-LABEL: func @merge_with_resource_type

  %m0 = mhlo.constant dense<0> : tensor<i32>
  %t0 = "tf.Const"() { value = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
  %m1 = mhlo.constant dense<1> : tensor<i32>
  %t1 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m2 = mhlo.constant dense<1> : tensor<i32>
  %t2 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m3 = mhlo.constant dense<1> : tensor<i32>

  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi32>) -> tensor<1xi32>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>
}

// -----

// CHECK-LABEL: func @cast_op_int32_qint32
func.func @cast_op_int32_qint32(%arg0: tensor<1xi32>) -> tensor<1x!tf_type.qint32> {

}

// CHECK-LABEL: FoldTranspose
func.func @FoldTranspose(%arg0: tensor<1x10x20x3xf32>) -> tensor<1x20x40x16xf32> {
  %cst = arith.constant dense<[1, 20, 40, 16]> : tensor<4xi32>
  %cst_0 = arith.constant dense<[2, 0, 1, 3]> : tensor<4xi32>
  %0 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.8527095877721169E-10>>, value = dense<0> : tensor<16xi32>} : () -> tensor<16x!quant.uniform<i32:f32, 1.8527095877721169E-10>>

  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32
  // CHECK: value = dense<[1, 2, 3, 16909060]> : tensor<4xi32>
  // Check bytes come back in the right order
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 16909060]> : tensor<4xi32> } : () -> tensor<4xi32>
  func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64

  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32
  // CHECK: value = dense<[1, 2, 3, 16909060]> : tensor<4xi32>
  // Check bytes come back in the right order
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 16909060]> : tensor<4xi32> } : () -> tensor<4xi32>
  func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64

    %cst = "tf.Const"() {value = dense<-1> : tensor<i64>} : () -> tensor<i64>
    %cst_0 = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
    %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %cst_2 = "tf.Const"() {value = dense<0.00235294132> : tensor<f32>} : () -> tensor<f32>

// -----

// CHECK-LABEL: func_with_tfl_attrs
func.func @func_with_tfl_attrs(%arg0: tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> attributes {tf.entry_function = {inputs = "arg0,arg1", outputs = "arg0"}} {
  return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

// -----

// There are cases where ODML converter relies on constants not being folded or