//CHECK-NEXT: return %0 : tensor<1x161x1xf32>
//CHECK-NEXT:}

func.func @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
  %0 = "vhlo.convert_v1" (%arg0) : (tensor<2xf64>) -> tensor<2xf32>
  return %0 : tensor<2xf32>
}

//CHECK:func.func private @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
//CHECK-NEXT: %0 = "vhlo.convert_v1"(%arg0) : (tensor<2xf64>) -> tensor<2xf32>
//CHECK-NEXT: return %0 : tensor<2xf32>
//CHECK-NEXT:}

    // CHECK-NOT: tf.BesselI0e
    %0 = "tf.BesselI0e"(%arg0) : (tensor<3xf16>) -> (tensor<3xf16>)
    %1 = "tf.BesselI0e"(%arg1) : (tensor<3xf32>) -> (tensor<3xf32>)
    %2 = "tf.BesselI0e"(%arg2) : (tensor<3xf64>) -> (tensor<3xf64>)
    func.return %0, %1, %2 : tensor<3xf16>, tensor<3xf32>, tensor<3xf64>
  }

  // CHECK-LABEL: bessel_i1e

func.func @testBroadcastGradientArgI64() -> (tensor<2xi64>, tensor<0xi64>) {
  %s0 = "tf.Const"() {value = dense<> : tensor<0xi64>} : () -> tensor<0xi64>
  %s1 = "tf.Const"() {value = dense<[2, 4]> : tensor<2xi64>} : () -> tensor<2xi64>
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) {} : (tensor<0xi64>, tensor<2xi64>) -> (tensor<2xi64>, tensor<0xi64>)

  %sizes = "tf.Const"() {value = dense<[1, 4]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %0 = "tf.Slice"(%arg0, %arg1, %sizes) : (tensor<3x4xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x4xi32>
  func.return %0 : tensor<1x4xi32>
}

// CHECK-LABEL: slice_variable_start_negsize
func.func @slice_variable_start_negsize(%arg0: tensor<3x4xi32>, %arg1: tensor<2xi32>) -> tensor<1x4xi32> {

    %range_nume = "tf.Sub"(%r_max_f64, %r_min_f64) : (tensor<1xf64>, tensor<1xf64>) -> tensor<1xf64>
    %range_deno = "tf.Sub"(%i8_max_f64, %i8_min_f64) : (tensor<f64>, tensor<f64>) -> tensor<f64>

    %scale_f64 = "tf.Div"(%range_nume, %range_deno) : (tensor<1xf64>, tensor<f64>) -> tensor<1xf64>
    %scale = "tf.Cast"(%scale_f64) : (tensor<1xf64>) -> tensor<1xf32>

    // Add comparison with minimum if needed

  %0 = mhlo.composite "aten.avg_pool2d.default" %arg0 {composite_attributes = {ceil_mode = false, count_include_pad = true, divisor_override = "py_None", kernel_size = dense<3> : tensor<2xi64>, padding = dense<0> : tensor<2xi64>, stride = dense<1> : tensor<2xi64>}, decomposition = @XlaCallModule_aten.avg_pool2d.default.impl_0} : (tensor<1x3x6x6xf32>) -> tensor<1x3x4x4xf32>
  %1 = "tf.Identity"(%0) {device = ""} : (tensor<1x3x4x4xf32>) -> tensor<*xf32>

  // CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[21, 4]> : tensor<2xi64>
  // CHECK: %[[v0:.*]] = "tf.Reshape"(%arg0, %[[cst]]) : (tensor<2x5x7x3xf32>, tensor<3xi64>) -> tensor<2x5x21xf32>
  // CHECK: %[[v1:.*]] = "tf.Reshape"(%arg1, %[[cst_1]]) : (tensor<7x3x4xf32>, tensor<2xi64>) -> tensor<21x4xf32>

  %0 = "mhlo.transpose"(%arg0) <{permutation = dense<[1, 0]> : tensor<2xi64>}> : (tensor<2x3xf32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>

// CHECK-LABEL:   transpose_2d
// CHECK-NEXT:    %0 = "tfl.pseudo_const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-NEXT:    %1 = "tfl.cast"(%0) : (tensor<2xi64>) -> tensor<2xi32>

  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8

  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8