fix python api bug

mindspore/compression/quant/qat.py

@@ -142,33 +142,33 @@ class QuantizationAwareTraining(Quantizer):
 
     Examples:
         >>> class LeNet5(nn.Cell):
-        >>>     def __init__(self, num_class=10, channel=1):
-        >>>         super(LeNet5, self).__init__()
-        >>>         self.type = "fusion"
-        >>>         self.num_class = num_class
-        >>>
-        >>>         # change `nn.Conv2d` to `nn.Conv2dBnAct`
-        >>>         self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
-        >>>         self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
-        >>>         # change `nn.Dense` to `nn.DenseBnAct`
-        >>>         self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
-        >>>         self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
-        >>>         self.fc3 = nn.DenseBnAct(84, self.num_class)
-        >>>
-        >>>         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        >>>         self.flatten = nn.Flatten()
-        >>>
-        >>>     def construct(self, x):
-        >>>         x = self.conv1(x)
-        >>>         x = self.max_pool2d(x)
-        >>>         x = self.conv2(x)
-        >>>         x = self.max_pool2d(x)
-        >>>         x = self.flatten(x)
-        >>>         x = self.fc1(x)
-        >>>         x = self.fc2(x)
-        >>>         x = self.fc3(x)
-        >>>         return x
-        >>>
+        ...     def __init__(self, num_class=10, channel=1):
+        ...         super(LeNet5, self).__init__()
+        ...         self.type = "fusion"
+        ...         self.num_class = num_class
+        ...
+        ...         # change `nn.Conv2d` to `nn.Conv2dBnAct`
+        ...         self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
+        ...         self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
+        ...         # change `nn.Dense` to `nn.DenseBnAct`
+        ...         self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
+        ...         self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
+        ...         self.fc3 = nn.DenseBnAct(84, self.num_class)
+        ...
+        ...         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        ...         self.flatten = nn.Flatten()
+        ...
+        ...     def construct(self, x):
+        ...         x = self.conv1(x)
+        ...         x = self.max_pool2d(x)
+        ...         x = self.conv2(x)
+        ...         x = self.max_pool2d(x)
+        ...         x = self.flatten(x)
+        ...         x = self.fc1(x)
+        ...         x = self.fc2(x)
+        ...         x = self.fc3(x)
+        ...         return x
+        ...
         >>> net = LeNet5()
         >>> quantizer = QuantizationAwareTraining(bn_fold=False, per_channel=[True, False], symmetric=[True, False])
         >>> net_qat = quantizer.quantize(net)