在运行推理代码时出现问题

代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_mlu
import torch_mlu.core.mlu_model as ct
import torch_mlu.core.mlu_quantize as mlu_quantize
import torchvision.models as models
ct.set_core_number(1)
ct.set_core_version("MLU270")

torch.set_grad_enabled(False)

class Net(nn.Module):
    def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout2d(0.25)
      self.dropout2 = nn.Dropout2d(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
      x = self.conv1(x)
      x = F.relu(x)
      x = self.conv2(x)
      x = F.relu(x)
      x = F.max_pool2d(x, 2)
      x = self.dropout1(x)
      x = torch.flatten(x, 1)
      x = self.fc1(x)
      x = F.relu(x)
      x = self.dropout2(x)
      x = self.fc2(x)
      output = F.log_softmax(x, dim=1)
      return output

net=Net().eval()
torch.save(net.state_dict(), 'test_weights.pth')
input_data = torch.rand((1,1,28,28), dtype=torch.float)

mean = [0]
std = [1/255]

net.load_state_dict(torch.load('test_weights.pth', map_location='cpu'), False)
net_quantization = mlu_quantize.quantize_dynamic_mlu(net, {'mean':mean, 'std':std, 'firstconv':True}, dtype='int8', gen_quant=True)
output = net_quantization(input_data)
torch.save(net_quantization.state_dict(), 'test_quantization.pth')

net_quantization.load_state_dict(torch.load('test_quantization.pth'))
net_mlu = net_quantization.to(ct.mlu_device())
input_mlu = input_data.to(ct.mlu_device())
output=net_mlu(input_mlu)
print(output.cpu())

您好，请问，这是错误是因为什么呢