#loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \22tf.Reshape\22(%arg0, %arg1) {_xla_outside_compilation = \220\22} : (tensor<3360x?xi32>, tensor<3xi32>) -> tensor<3x1120x?xi32> loc(#loc9)\0A    %1:3 = ...

  %cst_2 = arith.constant dense<[4, 1024, 4096]> : tensor<3xi32>
  %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst_1) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<4x1024x1024xbf16>, tensor<4096x1024xbf16>, none) -> tensor<4096x4096xbf16>
  %1 = "tfl.reshape"(%0, %cst_2) : (tensor<4096x4096xbf16>, tensor<3xi32>) -> tensor<4x1024x4096xbf16>
  func.return %1 : tensor<4x1024x4096xbf16>

      %outputs_9, %control_10 = tf_executor.island(%control_4) wraps "tf.Identity"(%outputs_7) {device = ""} : (tensor<2x3xi32>) -> tensor<2x3xi32>
      tf_executor.fetch %outputs_9 : tensor<2x3xi32>
    }
    return %0 : tensor<2x3xi32>
  }
}

// CHECK: module attributes
// CHECK-SAME:  tfl.metadata = {{{.*}}keep_stablehlo_constant = "true"{{.*}}}

// version.

// CHECK-LABEL: allows_chlo
func.func @allows_chlo(%arg0: tensor<1x32x10x32xi32>, %arg1: tensor<32xi32>) -> tensor<1x32x10x32xi32> {
  %0 = "chlo.broadcast_add"(%arg0, %arg1) {broadcast_dimensions = array<i64: 3>} : (tensor<1x32x10x32xi32>, tensor<32xi32>) -> tensor<1x32x10x32xi32>
  func.return %0 : tensor<1x32x10x32xi32>

  func.return %0 : tensor<2xi32>
}

// -----

// CHECK-LABEL: listFromTensor
func.func @listFromTensor(%tensor: tensor<3xi32>, %shape : tensor<?xi32>) -> tensor<!tf_type.variant<tensor<i32>>> {
  %0 = "tf.TensorListFromTensor"(%tensor, %shape) : (tensor<3xi32>, tensor<?xi32>) -> tensor<!tf_type.variant<tensor<i32>>>

func.func @testNotTfDialect(%arg0: tensor<1x32x10x32xi32>, %arg1: tensor<32xi32>) -> tensor<1x32x10x32xi32> {
 // expected-error@below {{op is in dialect chlo not in tf functional dialect}}
  %0 = "chlo.broadcast_add"(%arg0, %arg1) {broadcast_dimensions = array<i64: 3>} : (tensor<1x32x10x32xi32>, tensor<32xi32>) -> tensor<1x32x10x32xi32>
  func.return %0 : tensor<1x32x10x32xi32>
}

// -----

}

// -----

func.func @testNotTfDialect(%arg0: tensor<1x32x10x32xi32>, %arg1: tensor<32xi32>) -> tensor<1x32x10x32xi32> {
 // expected-error@below {{op is in dialect chlo which is not an accepted dialect}}
  %0 = "chlo.broadcast_add"(%arg0, %arg1) {broadcast_dimensions = array<i64: 3>} : (tensor<1x32x10x32xi32>, tensor<32xi32>) -> tensor<1x32x10x32xi32>
  func.return %0 : tensor<1x32x10x32xi32>

// -----

// Tests that int constants are not folded.

// CHECK-LABEL: transpose_int
func.func @transpose_int() -> tensor<3x2xi32> {
  %0 = stablehlo.constant dense<0> : tensor<2x3xi32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg

// CHECK: return %1 : tensor<5x2x3x4xf32>

// -----

// CHECK-LABEL: pushTposeAfterAddSimpleWithFold
func.func @pushTposeAfterAddSimpleWithFold(%arg0: tensor<2x3xi32>) -> tensor<3x2xi32> {
  %perm = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>

  // expected-error @+1 {{Node `mhlo.dynamic_iota` must have compile-time constant}}
  %3 = "mhlo.dynamic_iota"(%2) <{iota_dimension = 0 : i64}> : (tensor<1xi64>) -> tensor<?xi32>
  %4 = mhlo.multiply %3, %3 : tensor<?xi32>
  return %4 : tensor<?xi32>