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法律研究在 CSS3 中設定列規則
要設定列規則，可以使用速記的列規則屬性，可以在其中設定以下屬性：
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
列規則的值可以設定為：
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，這些屬性可以分開使用。我們來看看這兩個示例。
列規則速記屬性
在此示例中，我們使用速記屬性設定了列規則：
column-rule: 5px dotted orange;
以上示例將設定規則寬度為 5px、樣式為點線、顏色為橙色。
示例
現在我們來看一個示例：
<!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>
設定列規則
我們來看一個使用所有屬性的示例，這些屬性用於將列規則置於速記屬性的位置。這將設定列規則寬度、顏色和樣式：
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>
更新於：27-12-2023
130 次瀏覽
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