%6 = "tf.Div"(%arg1, %cst_2) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %7 = "tf.AddV2"(%6, %cst_1) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %8 = "tf.Maximum"(%7, %cst_1) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %9 = "tf.Minimum"(%8, %cst_4) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-unroll-batch-matmul %s | FileCheck %s

//==== V1 tests ====

func.func @batchMatMulTwoDim(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32>
  func.return %0 : tensor<2x3x4x6xf32>

  // CHECK-LABEL: batchMatMulTwoDim

  %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<1x2x3x4x5x6xf32>, tensor<*xi32>) -> tensor<7x8x1x2x3x4x5x6xf32>
  func.return %0: tensor<7x8x1x2x3x4x5x6xf32>

// CHECK-LABEL: broadcast_to_high_dim_with_unknown_shape
// CHECK:  [[BCT:%.*]] = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<1x2x3x4x5x6xf32>, tensor<*xi32>) -> tensor<7x8x1x2x3x4x5x6xf32>

}

// CHECK-LABEL: func @BiasAddGrad_NHWC
func.func @BiasAddGrad_NHWC(%arg0: tensor<2x3x4x5xf32>) -> tensor<5xf32> {
  // CHECK: "tf.Const"() <{value = dense<[0, 1, 2]> : tensor<3xi64>}>
  // CHECK: "tf.Sum"({{.*}}) <{keep_dims = false}>

  %0 = "tf.BiasAddGrad"(%arg0) {data_format = "NHWC"} : (tensor<2x3x4x5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
}

// CHECK-LABEL: func @BiasAddGrad_NCHW