// CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])
  // CHECK: %[[R1:.*]] = "tf.BiasAdd"(%[[R0]], %[[ARG1]]) <{data_format = "NHWC"}> {device = ""}
  // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]])

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x32x32x3xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x7x7x8xf32> {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NCHW"

  // CHECK:  %cst_2 = "tf.Const"() <{value = dense<0> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK-DAG:  %cst_3 = "tf.Const"() <{value = dense<[8, 8, 1, 1]> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK:  %cst_4 = "tf.Const"() <{value = dense<1> : tensor<4xi64>}> : () -> tensor<4xi64>

  func.return %0 : tensor<2x?x?x1xf32>
  // CHECK-LABEL: einsum_matmul_dynamic_size
  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<[2, -1, 1, 1]> : tensor<4xi64>
  // CHECK: %[[v0:.*]] = "tf.Reshape"(%arg1, %cst) : (tensor<2x?xf32>, tensor<4xi64>) -> tensor<2x?x1x1xf32>
  // CHECK: %[[v1:.*]] = "tf.BatchMatMulV2"(%arg0, %0) <{adj_x = false, adj_y = false}> : (tensor<2x?x?x?xf32>, tensor<2x?x1x1xf32>) -> tensor<2x?x?x1xf32>

  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

}

// -----

// If with a 4xi1 condition.

func.func private @testIf1Then(tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
func.func private @testIf1Else(tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

// CHECK-LABEL: func @testIf1x4
func.func @testIf1x4(tensor<4xi1>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> {
^bb0(%arg0: tensor<4xi1>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>):

func.func @testShapeN(%arg0: tensor<f32>, %arg1: tensor<1x32x32x16xf32>) -> (tensor<0xi64>, tensor<4xi64>) {

  // CHECK-DAG: %[[SHAPE0:.*]] = "tf.Const"() <{value = dense<> : tensor<0xi64>}>
  // CHECK-DAG: %[[SHAPE1:.*]] = "tf.Const"() <{value = dense<[1, 32, 32, 16]> : tensor<4xi64>}>
  %0:2 = "tf.ShapeN"(%arg0, %arg1) : (tensor<f32>, tensor<1x32x32x16xf32>) -> (tensor<0xi64>, tensor<4xi64>)

  // CHECK: return %[[SHAPE0]], %[[SHAPE1]]

  // CHECK-DAG: %[[MATMUL_RHS_SHAPE:.*]] = "tf.Const"() <{value = dense<[5, 6]> : tensor<2xi64>}>
  // CHECK-DAG: %[[RESULT_SHAPE:.*]] = "tf.Const"() <{value = dense<[2, 3, 4, 6]> : tensor<4xi64>}>

  // CHECK: %[[LHS_RESHAPED:.*]] = "tf.Reshape"(%arg0, %[[LHS_RESHAPED_SHAPE]]) : (tensor<2x3x4x5xf32>, tensor<3xi64>) -> tensor<6x4x5xf32>

  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.svdf"(%arg0, %arg1, %arg2, %arg3, %0) {fused_activation_function = "RELU", rank = 2 : i32} : (tensor<4xf32>, tensor<4xi8>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>