// -----

// CHECK-LABEL: func @einsum
func.func @einsum(%arg0: tensor<2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<2x4xf32> {
  // CHECK:  mhlo.einsum
  %0 = "tf.Einsum"(%arg0, %arg1) {equation = "ab,bc->ac"} : (tensor<2x3xf32>, tensor<3x4xf32>) -> tensor<2x4xf32>
  func.return %0: tensor<2x4xf32>
}

// -----

// CHECK-LABEL: func @unary_einsum

  // CHECK:  return
}

func.func @tile(tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> {
^bb0(%arg0: tensor<2x3xf32>, %arg1: tensor<2xi32>):
  %cst = "tf.Const"() { value = dense<[1, 2]> : tensor<2xi32> } : () -> tensor<2xi32>
  %0 = "tf.Tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32>
  func.return %0 : tensor<2x6xf32>

  // CHECK-LABEL: tile

  %30 = "tf.GatherV2"(%9, %29, %5) {batch_dims = 0 : i64, device = ""} : (tensor<32x1xf32>, tensor<2x16xi32>, tensor<i32>) -> tensor<2x16x1xf32>
  %31 = "tf.AddV2"(%19, %28) {device = ""} : (tensor<2x16xi32>, tensor<2x16xi32>) -> tensor<2x16xi32>
  %32 = "tf.GatherV2"(%9, %31, %5) {batch_dims = 0 : i64, device = ""} : (tensor<32x1xf32>, tensor<2x16xi32>, tensor<i32>) -> tensor<2x16x1xf32>

%0 = "mhlo.dot_general"(%arg0, %arg1) {
  dot_dimension_numbers = #mhlo.dot<
    lhs_batching_dimensions = [0, 1],
    rhs_batching_dimensions = [0, 1],
    lhs_contracting_dimensions = [3],
    rhs_contracting_dimensions = [3]
  >} : (tensor<2x?x2x3xf32>, tensor<2x?x4x3xf32>) -> tensor<2x?x2x4xf32>
func.return %0 : tensor<2x?x2x4xf32>

  %4 = "tfl.dequantize"(%3) : (tensor<32x12x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x12xf32>
  %5 = "tfl.fully_connected"(%2, %4, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<32x12xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

// Tests ops that variable shapes are correctly evaluated on static types.
// CHECK-LABEL: func @testVariableShape
func.func @testVariableShape(%arg0: tensor<!tf_type.resource<tensor<2x4xf32>>>) -> tensor<2xi32> {
  %0 = "tf.VariableShape"(%arg0) : (tensor<!tf_type.resource<tensor<2x4xf32>>>) -> tensor<2xi32>
  // CHECK:         [[cst:%.*]] = "tf.Const{{.*}} dense<{{\[}}2, 4]> : tensor<2xi32>
  // CHECK-NEXT:    return [[cst]] : tensor<2xi32>

}

// -----

func.func @testNonMaxSuppressionV4WithCorrectBoxShape(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>) -> (tensor<2xi32>, tensor<i32>) {

  %1 = mhlo.constant dense<[0.000000e+00, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01, 7.500000e-01, 2.500000e-01,...

    // CHECK-NOT: tf.TPUPartitionedOutputV2
    %partitioned_output:2 = "tf.TPUPartitionedOutputV2"(%computation) {_XlaSharding = "\08\03\1A\02\01\02\22\02\00\01", partition_dims = [1, 2]} : (tensor<3x4xf32>) -> (tensor<3x2xf32>, tensor<3x2xf32>)
    // CHECK: "tf.AssignVariableOp"(%arg0, %[[PARALLEL_EXECUTE_OUTPUT]]#0)

// Tests that a float `stablehlo.slice` is not converted to `tfl.slice`.

func.func @slice_float(%arg0: tensor<3x4xf32>) -> tensor<2x2xf32> {
  %0 = "stablehlo.slice"(%arg0) {
    start_indices = array<i64: 1, 2>,
    limit_indices = array<i64: 3, 4>,
    strides = array<i64: 1, 1>
  } : (tensor<3x4xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK-LABEL: slice_float
// CHECK-NOT: tfl.slice