func.func @einsum_basic(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>
  // CHECK-LABEL: einsum_basic
  // CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false}> : (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
}

  // CHECK: return %[[RESULT]] : tensor<2x3x4x6xf32>
}

// -----

func.func @batchMatMulOneDim(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>

  // CHECK-LABEL: batchMatMulOneDim
  // CHECK-DAG: %[[SPLITTING_AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}>

func.func @lower_einsum(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>
}
// CHECK-LABEL: lower_einsum
// CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false}> : (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>

  // CHECK-DAG: %[[MEAN_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[MEAN]], %[[X_SHAPE]]) <{broadcast_dimensions = dense<2> : tensor<1xi64>}> : (tensor<256xf32>, tensor<4xindex>) -> tensor<3x4x256x6xf32>
  // CHECK-DAG: %[[SQ_DIFF:.+]] = "tf.SquaredDifference"(%arg0, %[[MEAN_BCAST]]) : (tensor<3x4x256x6xf32>, tensor<3x4x256x6xf32>) -> tensor<3x4x256x6xf32>

  %3 = stablehlo.add %2, %1 : tensor<1x4x5x5xf32>
  %4 = stablehlo.maximum %3, %5 : tensor<1x4x5x5xf32>
  return %4 : tensor<1x4x5x5xf32>
}
// CHECK-DAG: %[[WEIGHT_CONST:.+]] = stablehlo.constant {{.*}} : tensor<3x3x2x4xf32>
// CHECK-DAG: %[[ZERO_CONST:.+]] = stablehlo.constant {{.*}} : tensor<1x5x5x4xf32>

static const char *const module_with_add =
    R"(module {
func.func @main(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x4x5xf32>) -> tensor<3x4x5xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<3x4x5xf32>, tensor<3x4x5xf32>) -> tensor<3x4x5xf32>
  func.return %0 : tensor<3x4x5xf32>
}
}
)";

// Test pass filter.
TEST(DataDumperLoggerConfig, TestPassFilter) {
  mlir::DialectRegistry mlir_registry;

func.func @xla_dot_v2(%arg0: tensor<?x2x3xf32>, %arg1: tensor<3x4x5xf32>) -> (tensor<?x2x4x5xf32>) {
  %0 = "tf.XlaDotV2"(%arg0, %arg1) {device = "", dimension_numbers = "\0A\01\02\12\01\00", precision_config = ""} : (tensor<?x2x3xf32>, tensor<3x4x5xf32>) -> tensor<?x2x4x5xf32>
  func.return %0 : tensor<?x2x4x5xf32>
}

// CHECK: func @xla_dot_v2

    %20 = "tf.Mul"(%19, %cst_0) {device = ""} : (tensor<2x4x3x6xf32>, tensor<f32>) -> tensor<2x4x3x6xf32>
    %21 = "tf.Relu"(%20) {device = ""} : (tensor<2x4x3x6xf32>) -> tensor<2x4x3x6xf32>
    %22 = "tf.Minimum"(%21, %cst) {device = ""} : (tensor<2x4x3x6xf32>, tensor<f32>) -> tensor<2x4x3x6xf32>
    %23 = "tf.Identity"(%22) {device = ""} : (tensor<2x4x3x6xf32>) -> tensor<2x4x3x6xf32>

  %0 = "quantfork.stats"(%arg0) {
    layerStats = dense<[0.0, 1.0]> : tensor<2xf32>
  } : (tensor<?x5x5x2xf32>) -> tensor<?x5x5x2xf32>
  %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3x5x5x2xf32>} : () -> tensor<3x5x5x2xf32>
  %2 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %3 = "tfl.conv_2d"(%0, %1, %2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,

  // CHECK-SAME: %[[ARG0:.*]]: tensor<3x4x5xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<3x5x2xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<2x3x2xi64>
  func.func private @composite_and_gather(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x2xf32>, %arg2: tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32> {
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[ARG0]]) {volatile} : (tensor<3x4x5xf32>) -> tensor<3x4x5x!quant.uniform<i8:f32, 5.000000e-03>>