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

    // 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

}

// CHECK-LABEL: func @testEmptyFloat
func.func @testEmptyFloat() -> (tensor<5xf64>) {
  %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK: [[VAL:%.+]] = "tf.Const"() <{value =  dense<0.000000e+00> : tensor<5xf64>}>
  // CHECK: return [[VAL]]
  %1 = "tf.Empty"(%0) : (tensor<i32>) -> (tensor<5xf64>)
  func.return %1 : tensor<5xf64>
}

// CHECK-LABEL: func @testEmptyf16

  %1 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %2 = "mhlo.pad"(%arg0, %1) {edge_padding_high = dense<[0, 0, 1, 1]> : tensor<4xi64>, edge_padding_low = dense<[0, 0, 1, 1]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x3x6x6xf32>, tensor<f32>) -> tensor<1x3x8x8xf32>
  %3 = "mhlo.reduce_window"(%2, %1) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):

// CHECK-LABEL: broadcast_to_to_reshape_i64
func.func @broadcast_to_to_reshape_i64(%arg0: tensor<4x4x4xf32>, %arg1 : tensor<4xi64>) -> tensor<1x4x4x4xf32> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<4x4x4xf32>, tensor<4xi64>) -> tensor<1x4x4x4xf32>
  // CHECK: "tfl.cast"
  // CHECK-SAME: (tensor<4xi64>) -> tensor<4xi32>
  // CHECK-NEXT: "tfl.reshape"
  // CHECK-SAME: (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32>

func.return %0 : tensor<2x?x2x4xf32>

// CHECK-LABEL:     convert_dot_general_dynamic_batch_dim
// CHECK:           %0 = "tfl.pseudo_const"() <{value = dense<[0, 1, 3, 2]> : tensor<4xi64>}> : () -> tensor<4xi64>
// CHECK-NEXT:      %1 = "tfl.cast"(%0) : (tensor<4xi64>) -> tensor<4xi32>
// CHECK-NEXT:      %2 = "tfl.transpose"(%arg1, %1) : (tensor<2x?x4x3xf32>, tensor<4xi32>) -> tensor<2x?x3x4xf32>

//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-NEXT: %15 = mhlo.constant dense<1.000000e+00> : tensor<f32>
    // CHECK-NEXT: %16 = mhlo.constant dense<[32, 12, 12, 64]> : tensor<4xi64>
    // CHECK-NEXT: %17 = "mhlo.rng"(%14, %15, %16) <{rng_distribution = #mhlo.rng_distribution<UNIFORM>}> : (tensor<f32>, tensor<f32>, tensor<4xi64>) -> tensor<32x12x12x64xf32>
  %cst = "tf.Const"() {value = dense<[32, 12, 12, 64]> : tensor<4xi32>} : () -> tensor<4xi32>

        %bdy_cast = "tf.Cast"(%barg0) : (tensor<*xi64>) -> tensor<4xf32>
        %bdy:2 = func.call @while_body(%bdy_cast, %barg1) : (tensor<4xf32>, tensor<i32>) -> (tensor<4xi64>, tensor<i32>)
        "tf.Yield"(%bdy#0, %bdy#1) : (tensor<4xi64>, tensor<i32>) -> ()
    }
  ) { is_stateless = false } : (tensor<*xi64>, tensor<i32>) -> (tensor<*xi64>, tensor<i32>)
  // CHECK: return [[Result]]#0
  func.return %0#0 : tensor<*xi64>