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法律研究在 CSS3 中设置列规则
要设置列规则，可以使用速记的列规则属性，可以在其中设置以下属性：
column-rule-width: set the width of the rule between columns column-rule-style: set the style of the rule between columns column-rule-color: set the rule of the rule between columns
列规则的值可以设置为：
column-rule: column-rule-width column-rule-style column-rule-color|initial|inherit; column-rule: column-rule-width column-rule-style column-rule-color|initial|inherit;
另外，这些属性可以分开使用。我们来看看这两个示例。
列规则速记属性
在此示例中，我们使用速记属性设置了列规则：
column-rule: 5px dotted orange;
以上示例将设置规则宽度为 5px、样式为点线、颜色为橙色。
示例
现在我们来看一个示例：
<!DOCTYPE html>
<html>
<head>
<style>
.demo {
column-count: 5;
-webkit-column-count: 5; /* Chrome, Safari, Opera */
-moz-column-count: 5; /* Firefox */
-webkit-column-gap: normal; /* Chrome, Safari, Opera */
-moz-column-gap: normal; /* Firefox */
column-gap: normal;
-webkit-column-rule: 5px dotted orange; /* Chrome, Safari, Opera */
-moz-column-rule: 5px dotted orange; /* Firefox */
column-rule: 5px dotted orange;
}
</style>
</head>
<body>
<h1>PyTorch</h1>
<div class="demo">
PyTorch is defined as an open source machine learning library for Python. It is used for applications such as natural language processing. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.
</div>
</body>
</html>
设置列规则
我们来看一个使用所有属性的示例，这些属性用于将列规则置于速记属性的位置。这将设置列规则宽度、颜色和样式：
column-rule-width: 5px; column-rule-color: blue; column-rule-style: double;
示例
示例：
<!DOCTYPE html>
<html>
<head>
<style>
.demo {
column-count: 4;
-webkit-column-count: 4; /* Chrome, Safari, Opera */
-moz-column-count: 4; /* Firefox */
-webkit-column-gap: normal; /* Chrome, Safari, Opera */
-moz-column-gap: normal; /* Firefox */
column-gap: normal;
-webkit-column-rule-width: 5px; /* Chrome, Safari, Opera */
-moz-column-rule-width: 5px; /* Firefox */
column-rule-width: 5px;
-webkit-column-rule-color: blue; /* Chrome, Safari, Opera */
-moz-column-rule-color: blue; /* Firefox */
column-rule-color: blue;
-webkit-column-rule-style: double; /* Chrome, Safari, Opera */
-moz-column-rule-style: double; /* Firefox */
column-rule-style: double;
}
</style>
</head>
<body>
<h1>PyTorch</h1>
<div class="demo">
PyTorch is defined as an open source machine learning library for Python. It is used for applications such as natural language processing. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.
</div>
</body>
</html>
更新于：27-12-2023
130 次浏览
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