// %0 = "tf.MaximumFloat"(%arg0, %arg1) : (tensor<*xi8>, tensor<*xi8>) -> tensor<*xi8>
  // %1 = "tf.DequantizeFloat"(%0) : (tensor<*xi8>) -> tensor<*xf32>
// CHECK: %[[MAXIMUMFLOAT_0:.*]] = "tf.MaximumFloat"(%arg0, %arg1) : (tensor<*xi8>, tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[CAST_0:.*]] = "tfr.cast"(%[[MAXIMUMFLOAT_0]]) : (tensor<*xi8>) -> !tfr.tensor

  // CHECK: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[QUANTIZE]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xi8>
  // CHECK: %[[CONVERT_2:.*]] = mhlo.bitcast_convert %[[CONVERT_1]] : (tensor<2xi8>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>
  // CHECK: %[[DEQUANTIZE:.*]] = mhlo.uniform_dequantize %[[CONVERT_2]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xf32>

}

// CHECK-LABEL: @cast_ui8_to_i8
func.func @cast_ui8_to_i8() -> tensor<4xi8> {
  %cst = arith.constant dense<[0, 255, 127, 128]> : tensor<4xui8>
  %0 = "tfl.cast"(%cst) : (tensor<4xui8>) -> tensor<4xi8>
  func.return %0 : tensor<4xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, -1, 127, -128]> : tensor<4xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i8_to_i32

// Identity function to make the exporter happy
func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> {
  func.return %arg0 : tensor<4xi8>
}

//CHECK:func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> attributes {tf.entry_function = {inputs = "arg0", outputs = "arg0"}} {
//CHECK: return %arg0 : tensor<4xi8>
//CHECK:}

func.func @testI8ToI4WithinRange() -> (tensor<4xi8>) {
  %0 = arith.constant dense<[-8, 0, 1, 7]> : tensor<4xi8>
  // expected-error@+1 {{type of return operand 0 ('tensor<4xi4>') doesn't match function result type ('tensor<4xi8>')}}
  func.return %0 : tensor<4xi8>
}

func.func @testI8ToI4NotWithinRange() -> tensor<4xi8> {
  %0 = arith.constant dense<[-10, 2, 3, 8]> : tensor<4xi8>
  func.return %0 : tensor<4xi8>

// CHECK-LABEL: @main
func.func @main(%arg0: tensor<4xi8>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi8>)
// CHECK: attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}}
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} {
  %0 = "tfl.neg"(%arg0) : (tensor<4xi8>) -> tensor<4xi8> loc("neg")
  %1 = "tfl.neg"(%arg1) : (tensor<4xi32>) -> tensor<4xi32> loc("neg")

func.func @preserve_assert(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi1> {
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1>
  "tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> ()
  func.return %0 : tensor<1xi1>
  // CHECK-LABEL: preserve_assert
  // CHECK: tfl.less_equal
  // CHECK: Assert
  // CHECK: return

  %0 = "tfl.less"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1>
  %1 = "tf.If"(%0, %arg0, %arg1) {else_branch = @cond_false, then_branch = @cond_true, is_stateless = false} : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %1 : tensor<1xf32>
}

  func.return
}

// CHECK-LABEL: @tf_if_gets_control_node
func.func @tf_if_gets_control_node(%arg0: tensor<1xi1>)->() {
 "tf.If"(%arg0) {_lower_using_switch_merge = true, _read_only_resource_inputs = [], device = "", else_branch = @noop, is_stateless = false, output_shapes = [#tf_type.shape<>], then_branch = @noop} : (tensor<1xi1>) -> ()
 func.return
}
// CHECK-NEXT: %[[CONTROL:.*]] = tfl.control_node controls "tf.If"
// CHECK-NEXT: return

  }

  func.func private @internal_conv3d_fn(
                         %input : tensor<*xf32>, %filter : tensor<*xi8>) -> tensor<*xf32> {

   // Use identity op to avoid the filter being constant-folded.
    %identity = "tf.Identity"(%filter) : (tensor<*xi8>) -> tensor<*xi8>
    %2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<*xi8>) -> tensor<*xf32>
    %3 = "tf.Conv3D"(%input, %2) {
      padding = "VALID", strides = [1, 1, 1, 1, 1],