func.func @testBitcast(%arg0: tensor<3x4xui16>) -> tensor<3x4x!tf_type.quint16> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x4xui16>) -> tensor<3x4x!tf_type.quint16>
  func.return %0 : tensor<3x4x!tf_type.quint16>
}

// -----

// CHECK-LABEL: func @testBitcast_v2
func.func @testBitcast_v2(%arg0: tensor<3x2xi16>) -> tensor<3xi32> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi16>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// -----

  DYNAMIC_UPDATE_SLICE = 151,
  RELU_0_TO_1 = 152,
  UNSORTED_SEGMENT_PROD = 153,
  UNSORTED_SEGMENT_MAX = 154,
  UNSORTED_SEGMENT_SUM = 155,
  ATAN2 = 156,
  UNSORTED_SEGMENT_MIN = 157,
  SIGN = 158,
  BITCAST = 159,
  BITWISE_XOR = 160,
  RIGHT_SHIFT = 161,
  // All Operators start with STABLEHLO_ prefixes are subject to change
  // Many of the ops below can not be executed by TFlite runtime

// CHECK-DAG: %[[MAX2:.+]] = arith.constant dense<3> : tensor<1xi64>
// CHECK: %[[BITCAST1:.+]] = "tfl.bitcast"(%[[ARG1]]) : (tensor<i64>) -> tensor<1xi64>
// CHECK: %[[MIN1:.+]] = "tfl.minimum"(%[[BITCAST1]], %[[MAX1]]) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
// CHECK: %[[BITCAST2:.+]] = "tfl.bitcast"(%[[ARG2]]) : (tensor<i64>) -> tensor<1xi64>

  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: func @bitcast
func.func @bitcast(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK:  mhlo.bitcast_convert %arg0 : (tensor<2xf32>) -> tensor<2xf32>
  %0 = "tf.Bitcast"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

  let hasFolder = 1;
}

def TFL_BitcastOp : TFL_Op<"bitcast", [Pure]> {
  let summary = "Bitcast operator";

  let description = [{
    Bitcasts a tensor from one type to another.
  }];

  let arguments = (ins AnyTensor:$input);

  let results = (outs AnyTensor:$output);

  // TFLite's bitcast bitop does not utilize options, instead derives types
  // from the TfLiteTensors.

  func.return %0 : tensor<?xf32>
}

// CHECK-LABEL:   func @bitcast(
// CHECK-SAME:                  %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<2xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Bitcast"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_1]] : tensor<2xf32>
// CHECK:         }
func.func @bitcast(%arg0: tensor<2xf32>) -> tensor<2xf32> {

            "Less", "LessEqual", "SigmoidGrad", "SoftplusGrad", "SoftsignGrad",
            "TanhGrad", "Pow", "SquaredDifference", "ApproximateEqual",
            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",
            "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad",
            "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select",

[..., sizeof(`type`)/sizeof(`T`)] to [...].

tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype
(e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()
gives module error.
For example,

Example 1:

>>> a = [1., 2., 3.]
>>> equality_bitcast = tf.bitcast(a, tf.complex128)
Traceback (most recent call last):
...

  // CHECK: return %0 : tensor<8xf32>
}


// -----

// CHECK-LABEL: testBitcast
func.func @testBitcast(%arg0: tensor<8xui32>) -> tensor<8xi32> {
  // CHECK: "tfl.bitcast"(%arg0)
  %0 = "tfl.bitcast"(%arg0) : (tensor<8xui32>) -> tensor<8xi32>
  func.return %0 : tensor<8xi32>
  // CHECK: return %0 : tensor<8xi32>
}

// -----

// CHECK-LABEL: testBitwiseXor

    "DYNAMIC_UPDATE_SLICE",
    "RELU_0_TO_1",
    "UNSORTED_SEGMENT_PROD",
    "UNSORTED_SEGMENT_MAX",
    "UNSORTED_SEGMENT_SUM",
    "ATAN2",
    "UNSORTED_SEGMENT_MIN",
    "SIGN",
    "BITCAST",
    "BITWISE_XOR",
    "RIGHT_SHIFT",
    "STABLEHLO_LOGISTIC",
    "STABLEHLO_ADD",
    "STABLEHLO_DIVIDE",
    "STABLEHLO_MULTIPLY",
    "STABLEHLO_MAXIMUM",
    "STABLEHLO_RESHAPE",