}

  for (const XlaOutputDescription& descr : compilation_result->outputs) {
    if (descr.type == DT_VARIANT) {
      return errors::Unimplemented(
          "Support for TensorList crossing the XLA/TF boundary "
          "is not implemented");
    }
  }

  std::vector<TensorShape> output_tensor_shapes;
  output_tensor_shapes.reserve(ctx->num_outputs());

      mlir::TF::RecvTPUEmbeddingActivationsOp::getOperationName(), context));
  supported_ops->insert(OperationName(
      mlir::TF::SendTPUEmbeddingGradientsOp::getOperationName(), context));
}

// Stack, TensorList and TensorArray ops are rewritten during the second phase
// of the bridge (compilation of TPUCompile op). They would not match any
// legalization/canonicalization pattern and have to be manually added to the

// Materialize the MlirPassthroughOp by replacing it with the MLIR module
// attached as an attribute.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMaterializePassthroughOpPass();

// Replicates the TensorList init op by undoing some CSE needed for correct
// shape assignment in shape_inference.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplicateTensorListInitOpsPass();

  *tensor_proto = TensorProtoHelpers::GenerateTensorProto();

  CreateAndCallKernelWithAttr(my_create_func, "TestKernelAttrTensor", v);
}

TEST_F(TestKernelAttr, TensorList) {
  struct TensorProtoHelpers {
   public:
    static ::tensorflow::TensorProto GenerateTensorProto1() {
      ::tensorflow::TensorProto tensor_proto;
      tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);

      "return getElementTypeOrSelf(element_type());">;

  let extraClassDeclaration = [{
    // Returns type of the TensorList element produced by this op.
    TensorType element_type() { return handle_dtype().getSubtypes()[0]; }

    // Returns data type of the result handle. Returned type contains type of
    // the TensorList element as a subtype.
    VariantType handle_dtype() {

  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    auto addn_op = cast<AddNOp>(op);

    // TODO(hinsu): Support variant with TensorList type. tf.AddV2 doesn't
    // support variant type so variant types require special handling.
    if (mlir::isa<VariantType>(getElementTypeOrSelf(addn_op.getType())))
      return failure();

  }];

  let arguments = (ins
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of gradients with which to update embedding tables.}]>:$gradients,
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of learning rates used for updating the embedding
tables via the optimizer. The length of the TensorList must be equal to the
number of dynamic learning rate tags specified in the

                              TF_TString_GetSize(t_out_tstr)));
  TF_DeleteTensor(t_out);
  TF_TString_Dealloc(&tstr[0]);
}

TEST_F(CApiAttributesTest, TensorList) {
  const char tensor1[] = {5, 7};
  const int64_t dims1[] = {1, 2};
  const size_t ndims1 = TF_ARRAYSIZE(dims1);

  const char tensor2[] = {2, 4, 6, 8};
  const int64_t dims2[] = {2, 2};

}

// -----

// expected-error@+1 {{More than one tfr.tensor_list result isn't allowed}}
tfr.func @Foo_multiple_tensor_list_results(%arg0: !tfr.tensor<C>,
              %arg1: !tfr.tensor_list<A>,
              %arg2: i32 {tfr.name = "A"},
              %arg3: vector<1xi32> {tfr.name = "C"}) ->
    (!tfr.tensor_list<A>, !tfr.tensor_list<A>) {

      CHECK:       }

      CHECK-LABEL: tfr.func @tf__test_identity_n_op(%x: !tfr.tensor_list) -> (!tfr.tensor_list) {
      CHECK-NEXT:   %[[raw_data:.*]] = tfr.quant_raw_data(%x) : (!tfr.tensor_list) -> (!tfr.tensor_list)
      CHECK:        tfr.return %[[raw_data:.*]] : !tfr.tensor_list
      CHECK:       }
    """
    self._check_code(mlir_code, mlir_code_exp)


if __name__ == '__main__':