auto etype = complex_type.getElementType();
    if (etype.isF32()) {
      tensorflow::TensorProto repr;
      repr.set_dtype(tensorflow::DT_COMPLEX64);

      tensorflow::TensorShapeProto* shape = repr.mutable_tensor_shape();
      shape->set_unknown_rank(false);
      shape->add_dim()->set_size(int64_t{1});
      std::string content;
      auto complex_value = std::complex<float>(static_cast<float>(value), 0.0f);

    // Attribute for that proto, and finally splat the Attribute.

    TensorProto tensor_copy = input_tensor;
    auto* shape = tensor_copy.mutable_tensor_shape();
    shape->clear_dim();
    shape->add_dim()->set_size(1);

    TF_ASSIGN_OR_RETURN(ElementsAttr single_attr,
                        ConvertTensorProto(tensor_copy, builder));

  // them 42. Our conversion routine should not materialize these elements when
  // creating the Attribute. If it tries to, we'll crash OOM here.
  TensorProto tensor;
  tensor.set_dtype(DT_FLOAT);
  tensor.mutable_tensor_shape()->add_dim()->set_size(1ULL << 35);
  tensor.add_float_val(42.0);

  mlir::MLIRContext context;
  mlir::Builder builder(&context);
  TF_ASSERT_OK_AND_ASSIGN(mlir::ElementsAttr attribute,

          DataType dtype = n->output_type(0);
          AddNodeAttr("dtype", dtype, &const_def);
          TensorProto value;
          value.set_dtype(dtype);
          value.mutable_tensor_shape()->add_dim()->set_size(
              shape_proto.dim_size());
          for (const auto& dim : shape_proto.dim()) {
            if (dtype == DT_INT32) {
              value.add_int_val(dim.size());

    const TensorT& tensor, const std::vector<uint8_t>& buffer) {
  tensorflow::TensorProto ret;
  ret.set_dtype(TflTypeToTfType(tensor.type));

  tensorflow::TensorShapeProto* shape = ret.mutable_tensor_shape();
  shape->set_unknown_rank(false);
  for (auto dim : tensor.shape) {
    shape->add_dim()->set_size(int64_t{dim});
  }
  // TensorFlow Lite uses tflite::DynamicBufer to encode vector of strings.

  tensorflow::TensorProto repr = tfl::ConvertTfliteConstTensor(tensor, buffer);
  repr.clear_tensor_shape();
  if (tfl::IsQuantized(tensor)) {
    repr.mutable_tensor_shape()->add_dim()->set_size(buffer.size());
    repr.set_dtype(tensorflow::DT_INT8);
  } else {
    repr.mutable_tensor_shape()->add_dim()->set_size(
        buffer.size() / (shaped_type.getElementTypeBitWidth() / CHAR_BIT));
  }

                         const TensorShapeProto& shape, Graph* graph_out) {
  DataType data_type = src_node->output_type(src_port);
  TensorProto dummy_proto;
  dummy_proto.set_dtype(data_type);
  *dummy_proto.mutable_tensor_shape() = shape;
  // Don't set any value field in the proto, since it is only going to be used
  // for shape inference.

  GraphDefBuilder::Options options(graph_out, /*status=*/nullptr);

  NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const",
                           opts.op_registry());
  TensorProto value;
  value.set_dtype(dtype);
  for (int dim : shape) {
    value.mutable_tensor_shape()->add_dim()->set_size(dim);
  }
  return opts.WithAttr("value", value)
      .WithAttr("dtype", dtype)
      .FinalizeBuilder(&node_builder);
}

Node* KnownShape(absl::Span<const int> shape,

  inputs.reserve(assets.size());
  for (const auto& asset : assets) {
    TensorInfo tensor_info;
    tensor_info.set_name(asset.tensor_name);
    tensor_info.set_dtype(DT_STRING);
    tensor_info.mutable_tensor_shape();
    inputs.push_back({asset.tensor_name, tensor_info});
  }

  std::unordered_map<std::string, std::string> tf_name_to_mlir_name;
  TF_ASSIGN_OR_RETURN(auto sub_module,