code: def test_float64_fallback():
      class ConvWithDtype(nn.HybridBlock):
          def __init__(self, dtype="float32", **kwargs):
              super(ConvWithDtype, self).__init__(**kwargs)
              self.weight = mx.gluon.Parameter(
                  "weight", dtype=dtype, allow_deferred_init=True
              )
              self.bias = mx.gluon.Parameter(
                  "bias", dtype=dtype, allow_deferred_init=True
              )
  
          def forward(self, x):
              out = mx.npx.convolution(
                  x,
                  kernel=(1, 1),
                  num_filter=3,
                  weight=self.weight.data(x.device),
                  no_bias=False,
                  bias=self.bias.data(x.device),
              )
              return out
  
          def infer_shape(self, x):
              self.weight.shape = (3, 3, 1, 1)
              self.bias.shape = (3,)
  
      dtype = "float64"
      net = ConvWithDtype(dtype=dtype)
      in_data = mx.np.random.normal(size=[3, 3, 3, 3], dtype=dtype)
      net.initialize()
      out = net(in_data)
      out.wait_to_read()
      assert in_data.dtype == out.dtype
  
imports: import copy
  import mxnet
  import pytest
  from subgraph_common import check_fusion, check_neg_fusion, check_quantize
  from subgraph_common import CustomNormalInit, DATA_SHAPE, RELU6, TailNegBlock
  from subgraph_common import DATA_SHAPE, SG_PASS_NAME, QUANTIZE_SG_PASS_NAME
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal, assert_almost_equal_with_err
label_desc: Use image node_id 1 for calling a global function with example usage: test_float64_fallback() and returns: out