//
// Examples:
//   * If `xla_gather_op_output_type` == tensor<*xf32>, then it returns:
//     tensor<*xf32>.
//   * If `xla_gather_op_output_type` == tensor<3x5xi32> and `collapsed_dims` ==
//     {0}, then it returns: tensor<1x3x5xi32>.
//   * If `xla_gather_op_output_type` == tensor<3x5xf32> and `collapsed_dims` ==
//     {1, 3}, then it returns: tensor<3x1x5x1xf32>.
Type GetSliceOpOutputType(Type xla_gather_op_output_type,

  // CHECK-NEXT: tf_mlrt.executeop([[y]]
  %v = "tf.AddV2"(%y, %z) {__op_key = 3: i32}: (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  return %w, %u, %v : tensor<1xi32>, tensor<1xi32>, tensor<1xi32>
}

// -----

// Test node names are preserved.

// CHECK-LABEL: func @main
func.func @main(%x: tensor<i32>) -> tensor<i32> {
  // CHECK: tf_mlrt.executeop

}

func.func @snapshot(%arg0: tensor<3xi32>) -> tensor<3xi32> {
  %0 = "tf.Snapshot"(%arg0) : (tensor<3xi32>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
  // Should be converted to Identity and then from Identity to value
  // CHECK-LABEL: snapshot
  // CHECK:  return %arg0 : tensor<3xi32>
}

func.func @stop_gradient(%arg0: tensor<3xi32>) -> tensor<3xi32> {

    %1 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?x3x4x2xf32>) -> tensor<i32>
    %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32>
    %3 = stablehlo.concatenate %2, %cst_0, %cst_1, %cst_2, dim = 0 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [0, 1, 2, 3] : (tensor<1x1x1x2xf32>, tensor<4xi32>) -> tensor<?x3x4x2xf32>

}

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();
  shape[dim] = dim_size;