code: def test_lstm_float64():
      """Tests if can handle other mixed precision types.
  
      As a toy example show can convert graph to float64 and have it run.
  
      It doesn't really make sense to do it, this just shows we can change
      the target mixed_precision_dtype.
      """
      units = 3
      iterations = 5
      mod, mod_params = lstm.get_workload(
          iterations=iterations, num_hidden=units
      )
  
      # This is an unrolled lstm so each data should be the previous results but
      # we don't care, we just want to stress test things.
      for i in range(iterations):
          mod_params[
              "data" if i == 0 else f"data{i}"
          ] = np.random.uniform(-10, 10, (1, units)).astype("float32")
  
      verify_mixed_precision_output_close(
          mod,
          mod_params,
          mixed_precision_dtype="float64",
          rtol=0.01,
          atol=0.01,
      )
  
imports: from typing import Any, Dict, List
  import numpy
  import pytest
  import tvm
  from tvm import relay
  from tvm.relay.testing import lstm
  from tvm.relay.transform import InferType, ToMixedPrecision, mixed_precision
label_desc: Use image node_id 4 for calling a global function with example usage: test_lstm_float64() without return types