func.return %r1 : tensor<100x100xf32>
}

// -----
// tf.Reshape with incorrect shape operand rank.
func.func @testReshape(%arg0: tensor<10x10x10xf32>, %shape1: tensor<2x2xi32>) -> tensor<*xf32> {
  // expected-error @+1 {{requires 'shape' to be rank 1, but got 2}}
  %r1 = "tf.Reshape" (%arg0, %shape1) : (tensor<10x10x10xf32>, tensor<2x2xi32>) -> tensor<*xf32>

func.func @testDiv(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
  // CHECK: _arithmetic_count = 1000 : i64
  %0 = "tfl.div"(%arg0, %arg1) {fused_activation_function = "NONE"} : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

func.func @testDivBroadcast(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10xf32>) -> tensor<10x10x10xf32> {

  func.return %0 : tensor<10x10xf32>
  // CHECK-LABEL: strided_slice_with_constant_attributes
  // CHECK-DAG: [[BEGIN:%cst.*]] = arith.constant dense<[-1, 0, 0]> : tensor<3xi32>

  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "GPU"} : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

// -----

// CHECK: tac.cost = 2.000000e+03

}

// -----

func.func @func_0_CPU(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10xf32>) -> tensor<10x10x10xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 1.000000e+03
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<10x10x10xf32>, tensor<10xf32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

// -----

  %5 = "tfl.log"(%4) : (tensor<10x1x10xf32>) -> tensor<10x1x10xf32>
  %6 = tfl.sub(%2, %5) {fused_activation_function = "NONE"} : (tensor<10x10x10xf32>, tensor<10x1x10xf32>) -> tensor<10x10x10xf32>
  return %6 : tensor<10x10x10xf32>
 // CHECK-NOT: "tfl.log_softmax"(%arg0) : (tensor<10x10x10f32>) -> tensor<10x10x10xf32>
}

// CHECK-SAME:     interior_padding = dense<0> : tensor<3xi64>
// CHECK-SAME: }> : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<10x10x10xf32>
// CHECK:      return %[[RES]] : tensor<10x10x10xf32>
}

// -----

// CHECK-LABEL: testTwoConsecutivePadsTwoNegativeHighPad
func.func @testTwoConsecutivePadsTwoNegativeHighPad(%arg0: tensor<10x10x10xf32>) -> (tensor<9x9x9xf32>) {
  %0 = mhlo.constant dense<0.000000e+00> : tensor<f32>

  func.return %0 : tensor<10x10xf32>

func.func @unfold_large_constant_splat() -> (tensor<10x10xf32>, tensor<1000x1000xf32>) {
  %0 = arith.constant dense<0.00000e+00> : tensor<10x10xf32>
  %1 = arith.constant dense<1.00000e+00> : tensor<1000x1000xf32>
  func.return %0, %1 : tensor<10x10xf32>, tensor<1000x1000xf32>

  // CHECK-DAG: %cst = arith.constant dense<0.000000e+00> : tensor<10x10xf32>
  // CHECK-DAG: %cst_0 = arith.constant dense<1000> : tensor<2xi64>

# CHECK:"tf.MlirPassthroughOp"
# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"