graph->NewName(absl::StrCat("var_shape_", var_node->name())));
          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) {

            else -> null
        }
    }

    private fun ConstantValueKind.toLiteralExpression(source: KtSourceElement?, value: Any?): FirLiteralExpression =
        buildLiteralExpression(source, this, value, setType = false)

    private fun FirFunctionCall.getOriginalFunction(): FirCallableDeclaration? {
        val symbol: FirBasedSymbol<*>? = when (val reference = calleeReference) {

        return generatorsByLanguage.get(language);
    }

    @Option(option = "type", description = "Set the type of project to generate.")
    public void setType(String type) {
        this.type = type;
    }

    @OptionValues("type")
    public List<String> getAvailableBuildTypes() {
        return getProjectLayoutRegistry().getAllTypes();
    }

    /**

    if (!mlir::cast<ShapedType>(lhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(rhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(cond.getType()).hasStaticShape()) {
      return rewriteOpWithDynamicInput(op, rewriter);
    }

    auto lhs_shape = mlir::cast<ShapedType>(lhs.getType()).getShape();
    auto rhs_shape = mlir::cast<ShapedType>(rhs.getType()).getShape();

  validate_indices:bool;
}

table EqualOptions {
}

table NotEqualOptions {
}

table ShapeOptions {
  // Optional output type of the operation (int32 or int64). Defaults to int32.
  out_type : TensorType;
}

table RankOptions {
}

table PowOptions {
}

table FakeQuantOptions {
  // Parameters supported by version 1:
  min:float;
  max:float;
  num_bits:int;