code: def input_mod():
      return tvm.relay.parse(
          """
          #[version = "0.0.5"]
          def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) {
            %0 = nn.dense(%x0, meta[relay.Constant][0], units=2304);
            %1 = add(%0, meta[relay.Constant][1]);
            %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16],
                    Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] {
              %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16],
                       PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] {
                nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True)
              };
              %6(%y_3_i0, %y_3_i1)
            };
            %3 = %2(%x3, meta[relay.Constant][2]);
            (%1, %3)
          }
          """,
          "from_string",
          None,
          metatable,
      )
  
imports: import tvm
  import tvm.testing
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: input_mod() and returns: tvm