This assumes that the ``cached_property`` ``G.node`` should be reset whenever ``G._node`` is set to a new value.
时间: 2024-05-25 20:08:00 浏览: 13
这句话的意思是假设在一个Python类中,存在一个名为`cached_property`的装饰器修饰了`G.node`属性,那么当`G._node`属性被赋予一个新值时,`G.node`这个缓存的属性应该被重置。具体来说,这个装饰器会将属性`G.node`的值缓存起来,以提高访问效率。但是,当`G._node`这个属性被修改时,为了避免缓存的旧值对程序产生影响,需要重置缓存的值。
相关问题
Implement the function that searches a given value x in linked list headed with node nil.
Sure, here's an implementation of the function in Python:
```
class Node:
def __init__(self, value):
self.value = value
self.next = None
def search_linked_list(nil, x):
node = nil.next
while node is not None:
if node.value == x:
return node
node = node.next
return None
```
Here, `nil` is the head of the linked list, which is typically a dummy node with no value that serves as a placeholder. The `search_linked_list` function takes in `nil` and the value `x` to search for, and returns the node in the linked list that contains `x`, or `None` if `x` is not found.
The function works by starting at the first node after `nil`, and iterating through the linked list until it finds the node with the value `x`. If it traverses the entire linked list without finding `x`, it returns `None`.
Note that this implementation assumes that the values in the linked list are unique. If there can be multiple nodes with the same value, you may need to modify the function to return a list of nodes instead of just one.
self.transform(img_c).unsqueeze(0).cuda()
This code snippet is using PyTorch to transform an image and then sending it to the GPU.
`self.transform` is likely an instance of a `torchvision.transforms` object, which is used to apply various image transformations such as resizing, cropping, flipping, rotation, normalization, etc.
`img_c` is likely a PIL (Python Imaging Library) image object.
`self.transform(img_c)` applies the transformations to the image and returns a transformed image.
`.unsqueeze(0)` adds a new dimension to the tensor at index 0. This is done because PyTorch expects input tensors to have a batch dimension, even if the batch size is 1.
`.cuda()` sends the tensor to the GPU for faster processing. This assumes that a CUDA-enabled GPU is available and PyTorch is configured to use it.
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