%8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xf16>, tensor<4xf16>) -> tensor<4xf16>

  func.return %5, %6, %7, %8 : tensor<f16>, tensor<4xf16>, tensor<4xf16>, tensor<4xf16>
}

// CHECK-LABEL: @elementwise_unary_ops
func.func @elementwise_unary_ops() -> (tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>) {

}

// CHECK-LABEL: func @testEmptyf16
func.func @testEmptyf16() -> (tensor<5xf16>) {
  %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK: [[VAL:%.+]] = "tf.Const"() <{value =  dense<0.000000e+00> : tensor<5xf16>}>
  // CHECK: return [[VAL]]
  %1 = "tf.Empty"(%0) : (tensor<i32>) -> (tensor<5xf16>)
  func.return %1 : tensor<5xf16>
}

// CHECK-LABEL: func @testEmptybf16

}

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

}

// CHECK-LABEL: func @testSumFoldBypass
func.func @testSumFoldBypass(%arg0: tensor<4x?xf16>, %arg1: tensor<*xi64>) -> tensor<4x?xf16> {
    // CHECK: return %arg0
  %0 = "tf.Sum"(%arg0, %arg1) { keep_dims = false }: (tensor<4x?xf16>, tensor<*xi64>) -> tensor<4x?xf16>
  func.return %0 : tensor<4x?xf16>
}

// CHECK-LABEL: @testMatrixDiag
func.func @testMatrixDiag(%diag: tensor<2x4xf32>) -> tensor<2x4x4xf32> {

}

// -----

func.func private @testIfThen(tensor<*xf16>) -> tensor<*xf32>
func.func private @testIfElse(tensor<*xf32>) -> tensor<*xf32>

// Test invalid tf.If operation
func.func @testInvalidIfOp(tensor<i1>, tensor<2xf32>) -> tensor<2xf32> {
^bb0(%arg0: tensor<i1>, %arg1: tensor<2xf32>):
  // expected-error @+1 {{'tf.If' op 'then_branch' input type tensor<*xf16> is incompatible with input type tensor<2xf32> at index 0}}

  %0 = "tf.Pad"(%arg0, %padding) : (tensor<3xf32>, tensor<1x2xi64>) -> tensor<6xf32>
  func.return %0 : tensor<6xf32>
}

// CHECK-LABEL: func @pad_bf16
func.func @pad_bf16(%arg0: tensor<3xbf16>) -> tensor<6xbf16> {
  %padding = "tf.Const"() { value = dense<[[1, 2]]> : tensor<1x2xi64> } : () -> tensor<1x2xi64>
  // CHECK-DAG: [[PAD:%.+]] = "tf.Const"() {{.+}}  -> tensor<1x2xi64>

func.func @broadcast_to_i16_low_dim(%arg0: tensor<3xi16>, %arg1: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  return %0 : tensor<3x3xi16>
  // CHECK:  %cst = arith.constant dense<1> : tensor<3x3xi16>
  // CHECK:  %0 = tfl.mul(%arg0, %cst) <{fused_activation_function = "NONE"}> : (tensor<3xi16>, tensor<3x3xi16>) -> tensor<3x3xi16>

	0xf0c: 0x30bd, 0xf0d: 0x30dd, 0xf0e: 0x30fd, 0xf0f: 0x0040, 0xf10: 0x0018, 0xf11: 0x0018,
	0xf12: 0x311d, 0xf13: 0x313d, 0xf14: 0x315d, 0xf15: 0x317d, 0xf16: 0x319d, 0xf17: 0x31bd,
	0xf18: 0x31dd, 0xf19: 0x31fd, 0xf1a: 0x321d, 0xf1b: 0x323d, 0xf1c: 0x315d, 0xf1d: 0x325d,
	0xf1e: 0x327d, 0xf1f: 0x329d, 0xf20: 0x0008, 0xf21: 0x0008, 0xf22: 0x0008, 0xf23: 0x0008,

	0xf0c: 0x30bd, 0xf0d: 0x30dd, 0xf0e: 0x30fd, 0xf0f: 0x0040, 0xf10: 0x0018, 0xf11: 0x0018,
	0xf12: 0x311d, 0xf13: 0x313d, 0xf14: 0x315d, 0xf15: 0x317d, 0xf16: 0x319d, 0xf17: 0x31bd,
	0xf18: 0x31dd, 0xf19: 0x31fd, 0xf1a: 0x321d, 0xf1b: 0x323d, 0xf1c: 0x315d, 0xf1d: 0x325d,
	0xf1e: 0x327d, 0xf1f: 0x329d, 0xf20: 0x0008, 0xf21: 0x0008, 0xf22: 0x0008, 0xf23: 0x0008,

	0xf0c: 0x0018, 0xf0d: 0x0018, 0xf0e: 0x0018, 0xf0f: 0x0018, 0xf10: 0x333d, 0xf11: 0x3761,
	0xf12: 0x3779, 0xf13: 0x3791, 0xf14: 0x37a9, 0xf15: 0x37c1, 0xf16: 0x37d9, 0xf17: 0x37f1,
	0xf18: 0x3809, 0xf19: 0x3821, 0xf1a: 0x3839, 0xf1b: 0x3851, 0xf1c: 0x3869, 0xf1d: 0x3881,
	0xf1e: 0x3899, 0xf1f: 0x38b1, 0xf20: 0x335d, 0xf21: 0x337d, 0xf22: 0x339d, 0xf23: 0x33bd,