add a blank line

Author: SigureMo

docs/api/paddle/vision/models/ResNet_cn.rst

@@ -5,7 +5,8 @@ ResNet
 
 .. py:class:: paddle.vision.models.ResNet(Block, depth=50, width=64, num_classes=1000, with_pool=True, groups=1)
 
- ResNet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+ResNet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnet101_cn.rst

@@ -5,7 +5,8 @@ resnet101
 
 .. py:function:: paddle.vision.models.resnet101(pretrained=False, **kwargs)
 
- 101层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+101层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnet152_cn.rst

@@ -5,7 +5,8 @@ resnet152
 
 .. py:function:: paddle.vision.models.resnet152(pretrained=False, **kwargs)
 
- 152层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+152层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnet18_cn.rst

@@ -5,7 +5,8 @@ resnet18
 
 .. py:function:: paddle.vision.models.resnet18(pretrained=False, **kwargs)
 
- 18层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+18层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnet34_cn.rst

@@ -5,7 +5,8 @@ resnet34
 
 .. py:function:: paddle.vision.models.resnet34(pretrained=False, **kwargs)
 
- 34层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+34层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnet50_cn.rst

@@ -5,7 +5,8 @@ resnet50
 
 .. py:function:: paddle.vision.models.resnet50(pretrained=False, **kwargs)
 
- 50层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
+
+50层的resnet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnext101_32x4d_cn.rst

@@ -5,7 +5,8 @@ resnext101_32x4d
 
 .. py:function:: paddle.vision.models.resnext101_32x4d(pretrained=False, **kwargs)
 
- ResNeXt-101 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
+
+ResNeXt-101 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnext101_64x4d_cn.rst

@@ -5,7 +5,8 @@ resnext101_64x4d
 
 .. py:function:: paddle.vision.models.resnext101_64x4d(pretrained=False, **kwargs)
 
- ResNeXt-101 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
+
+ResNeXt-101 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnext152_32x4d_cn.rst

@@ -5,7 +5,8 @@ resnext152_32x4d
 
 .. py:function:: paddle.vision.models.resnext152_32x4d(pretrained=False, **kwargs)
 
- ResNeXt-152 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
+
+ResNeXt-152 32x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
 
 参数
 :::::::::

docs/api/paddle/vision/models/resnext152_64x4d_cn.rst

@@ -5,7 +5,8 @@ resnext152_64x4d
 
 .. py:function:: paddle.vision.models.resnext152_64x4d(pretrained=False, **kwargs)
 
- ResNeXt-152 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
+
+ResNeXt-152 64x4d模型，来自论文 `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ 。
 
 参数
 :::::::::