self._setitem_single_column(loc, value, pi)

I'm sorry, as an AI language model, I cannot provide information about specific code snippets without additional context. Can you please provide more information about the programming language, framework, and context in which this code snippet is being used?

相关问题

Traceback (most recent call last): File "E:/403/myworld/GraphNet.py", line 14, in <module> g.ndata['feat'] = feat File "D:\code\myworld\lib\site-packages\dgl\view.py", line 99, in __setitem__ self._graph._set_n_repr(self._ntid, self._nodes, {key: val}) File "D:\code\myworld\lib\site-packages\dgl\heterograph.py", line 4346, in _set_n_repr if F.context(val) != self.device: File "D:\code\myworld\lib\site-packages\dgl\backend\pytorch\tensor.py", line 99, in context return input.device AttributeError: 'numpy.ndarray' object has no attribute 'device' 进程已结束,退出代码1

这个错误发生在使用 DGL 库设置节点属性时，因为传入的特征值是 Numpy 数组，而不是 PyTorch 张量。可以将 Numpy 数组转换为 PyTorch 张量，例如：

import torch
import numpy as np

feat = np.ones((10, 16))
feat_tensor = torch.from_numpy(feat)
g.ndata['feat'] = feat_tensor

这里我们将 Numpy 数组 `feat` 转换为 PyTorch 张量 `feat_tensor`，然后将其设置为节点特征。

__setattr__ = dict.__setitem__

在提供的代码中，`__setattr__`方法被重写为`dict.__setitem__`，这意味着当我们给对象的属性赋值时，实际上是调用了`dict.__setitem__`方法来设置字典中的键值对。这样做的目的是为了让对象支持通过点操作符来设置属性，使得对象可以像访问属性一样访问字典的键值对。这种做法在某些情况下可以提供更直观和方便的使用方式。

除了`__setattr__`方法外，还有`__getattr__`和`__delattr__`方法。`__getattr__`方法被重写为`dict.get`方法，当属性不存在时，会返回None，而不是抛出AttributeError异常。`__setattr__`方法被重写为`dict.__setitem__`，用于设置属性值。`__delattr__`方法被重写为`dict.__delitem__`，用于删除属性。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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