// CHECK: %[[v1:.*]] = "tf.Transpose"(%arg1, %[[cst_1]]) : (tensor<2x4x7x3xf32>, tensor<4xi32>) -> tensor<2x7x3x4xf32>
  // CHECK: "tf.BatchMatMulV2"(%[[v0]], %[[v1]]) <{adj_x = false, adj_y = false}> : (tensor<2x7x5x3xf32>, tensor<2x7x3x4xf32>) -> tensor<2x7x5x4xf32>
}

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

// -----

func.func @batchMatMulTwoDimAdjXY(%arg0: tensor<2x3x5x4xf32>, %arg1: tensor<2x3x6x5xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {adj_x = true, adj_y = true} : (tensor<2x3x5x4xf32>, tensor<2x3x6x5xf32>) -> tensor<2x3x4x6xf32>
  func.return %0 : tensor<2x3x4x6xf32>

  // CHECK-LABEL: batchMatMulTwoDimAdjXY

// CHECK: %[[ADD:.+]] = stablehlo.add %[[CONV]], %[[BIAS_CONST]] : tensor<1x5x5x4xf32>
// CHECK: %[[MAX:.+]] = stablehlo.maximum %[[ADD]], %[[ZERO_CONST]] : tensor<1x5x5x4xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[MAX]], dims = [0, 3, 1, 2] : (tensor<1x5x5x4xf32>) -> tensor<1x4x5x5xf32>
// CHECK: return %[[TRANSPOSE_1]]

// -----

}

// -----

func.func @trivial_dynamic_update_slice(%arg0: tensor<2x7x14xf32>, %arg1: tensor<2x7x14xf32>) -> tensor<2x7x14xf32> {
  %0 = arith.constant dense<0> : tensor<3xi32>
  %1 = "tfl.dynamic_update_slice"(%arg0, %arg1, %0) : (tensor<2x7x14xf32>, tensor<2x7x14xf32>, tensor<3xi32>) -> tensor<2x7x14xf32>
  // CHECK: return %arg1
  func.return %1 : tensor<2x7x14xf32>
}

// -----

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5x7xf32>) -> tensor<2x3x5x7xf32> {
  // expected-error @+1 {{requires bias operand to have rank exactly one}}
  %0 = "tf.BiasAdd"(%arg0, %arg1) {data_format = "NHWC"} : (tensor<2x3x5x7xf32>, tensor<5x7xf32>) -> tensor<2x3x5x7xf32>
  func.return %0 : tensor<2x3x5x7xf32>
}

// -----

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5xf32>) -> tensor<2x3x5x7xf32> {

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]])
// CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[o, 0, 1, i]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64}
// CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<2x3x3x4xf32>) -> tensor<1x3x3x2xf32>
// CHECK: return %[[CONV]]

// -----

  %0 = "tfl.slice"(%arg0, %begin0, %shape0) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<2x3x4x4xf32>
  %1 = "tfl.slice"(%arg0, %begin1, %shape1) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<1x2x3x4xf32>
  func.return %0, %1 : tensor<2x3x4x4xf32>, tensor<1x2x3x4xf32>
  // CHECK-DAG: %[[BEGIN_0:.*]] = arith.constant dense<0> : tensor<4xi64>

// CHECK:           })
// CHECK-SAME:        -> tensor<2x3x5x7xf32>
// CHECK:           [[COUNT:%.+]] = mhlo.constant dense<4.000000e+00> : tensor<f32>
// CHECK:           [[DIV_RESULT:%.+]] = chlo.broadcast_divide [[DIVIDEND]], [[COUNT]]
// CHECK-SAME:        broadcast_dimensions = array<i64>
// CHECK-SAME:        -> tensor<2x3x5x7xf32>
// CHECK:           [[CONV16:%.+]] = mhlo.convert [[DIV_RESULT]]