let hasOptions = 1;
}

def TFL_DynamicUpdateSliceOp: TFL_Op<"dynamic_update_slice", [
    Pure,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>]> {
  let summary = "DynamicUpdateSlice.";
  let description = [{
    DynamicUpdateSlice op that have the same semantics with XLA
    DynamicUpdateSlice.
    Generates a result which is the value of the input array

                                        {}, indices_type.getElementType()));
    // Note on zero_attr integer type: DynamicUpdateSlice op start_indices are
    // required to have matching types. This rewrite rule creates
    // DynamicUpdateSlice ops where the first "start index" is always i32 and
    // subsequent ones are constructed based on zero_attr. Thus the type

  // through TF for some of the ops. Some conversions are only supported through
  // this path.
  bool disable_hlo_to_tfl_conversion = false;
  // Whether to enable to use DynamicUpdateSlice op.
  bool enable_dynamic_update_slice = false;
  // Whether to preserve AssertOp during legalization.
  bool preserve_assert_op = false;
  // Whether to enable TF->stablehlo passes.

  // Assert that the tensor list operation is lowered to something that doesn't
  // get stuck on a broken dynamic update slice.
  ASSERT_THAT(compilation_result,
              Not(ComputationProtoContains("%.*=.*DynamicUpdateSlice")));
}

TEST(LegalizeTFTest, LegalizesFunctionWithBoundedDynamicArg) {
  static constexpr char kMlirModuleWithBoundedDynamicArgStr[] = R"(

        "tensorlist op lowering (default true)">,
      Option<"enable_dynamic_update_slice_", "enable-dynamic-update-slice",
             "bool", "false", "When specified to true, lower TensorListSetItem with "
             "DynamicUpdateSlice op (default false).">,
  ];
}

def ModifyIONodesPass : Pass<"tfl-modify-io-nodes", "mlir::func::FuncOp"> {
  let summary = "Modify the type of the model io nodes";
  let description = [{

    "READ_VARIABLE",
    "ASSIGN_VARIABLE",
    "BROADCAST_ARGS",
    "RANDOM_STANDARD_NORMAL",
    "BUCKETIZE",
    "RANDOM_UNIFORM",
    "MULTINOMIAL",
    "GELU",
    "DYNAMIC_UPDATE_SLICE",
    "RELU_0_TO_1",
    "UNSORTED_SEGMENT_PROD",
    "UNSORTED_SEGMENT_MAX",
    "UNSORTED_SEGMENT_SUM",
    "ATAN2",
    "UNSORTED_SEGMENT_MIN",
    "SIGN",
    "BITCAST",

        op, input.getType(), scalar_zero,
        ArrayRef<Value>({slice1, expanded_item, slice2}));
    return success();
  }

  // This function rewrites the original op into a XLA DynamicUpdateSlice op.
  // |item| is expanded to have the same dimension as input_handle and
  // |index| is expanded to [index, 0, 0, ...] as the indices to input_handle.
  // On a high level, it's doing something like:

}

def TF_XlaDynamicUpdateSliceOp : TF_Op<"XlaDynamicUpdateSlice", [Pure, TF_NoConstantFold]> {
  let summary = "Wraps the XLA DynamicUpdateSlice operator, documented at";

  let description = [{
https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice
.

XlaDynamicUpdateSlice generates a result which is the value of the `input`

        ML authoring is generally discouraged.
    *   Add support for unsigned 16-bit integer tensor types in cast op.
    *   Experimental support for lowering `list_ops.tensor_list_set_item` with
        `DynamicUpdateSlice`.
    *   Enabled a new MLIR-based dynamic range quantization backend by default
        *   The new backend is used for post-training int8 dynamic range
            quantization and post-training float16 quantization.