def gray_read(img_path: str | Path) -> Tensor: TypeError: unsupported operand type(s) for |: 'type' and 'type'

This error occurs when the `|` operator is used between two type objects. In this case, `str` and `Path` are both types, and the `|` operator is not defined for them.

To fix this error, you can either use a comma to separate the two types instead of `|`:

def gray_read(img_path: (str, Path)) -> Tensor:

Or you can import the `Union` type from the `typing` module and use it to specify multiple types:

from typing import Union

def gray_read(img_path: Union[str, Path]) -> Tensor:

Either way, this will allow you to specify that the `img_path` argument can be either a string or a `Path` object.

相关问题

TypeError: unsupported operand type(s) for *: 'Tensor' and 'collections.defaultdict'

引用\[1\]:TypeError: unsupported operand type(s) for -: ‘int’ and ‘list’ 解决方法 使用np.array进行type转换 import numpy as np y_true = \[\[0.\], \[1.\]\] 1-np.array(y_true) 以上就是全部内容。引用\[2\]:Error Discreptions: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-57-76c361767a88> in <module> ----> 1 p0V,p1V,pAb=bayes.trainNB0(trainMat,listClasses) D:\maxwelllearning\maxwellhandon\machine learning in action\bayes.py in trainNB0(trainMatrix, trainCategory) 38 p1Denom += sum(trainMatrix\[i\]) #Vector addition 39 else: ---> 40 p0Num += trainMatrix\[i\] 41 p0Denom += sum(trainMatrix\[i\]) 42 p1Vect = log(p1Num/p1Denom) # change to log() TypeError: unsupported operand type(s) for +=: 'float' and 'list' 。引用\[3\]:Modified Code: def trainNB0(trainMatrix,trainCategory): numTrainDocs = len(trainMatrix) numWords = len(trainMatrix\[0\]) pAbusive = sum(trainCategory)/float(numTrainDocs) p0Num = ones(numWords);p1Num = ones(numWords) #Initialize probabilities p0Denom = 2.0 ; p1Denom = 2.0 for i in range(numTrainDocs): if trainCategory\[i\] == 1: p1Num += trainMatrix\[i\] p1Denom += sum(trainMatrix\[i\]) #Vector addition else: p0Num += trainMatrix\[i\] p0Denom += sum(trainMatrix\[i\]) p1Vect = log(p1Num/p1Denom) # change to log() p0Vect = log(p0Num/p0Denom) # change to log() return p0Vect,p1Vect,pAbusive # Element-wise division。问题:TypeError: unsupported operand type(s) for *: 'Tensor' and 'collections.defaultdict'
回答: 这个错误是由于在代码中使用了不兼容的操作数类型导致的。具体来说，TypeError: unsupported operand type(s) for *: 'Tensor' and 'collections.defaultdict'错误表明在代码中使用了Tensor和collections.defaultdict之间不支持的乘法操作。要解决这个问题，你需要检查代码中的乘法操作，并确保操作数的类型是兼容的。你可能需要对其中一个操作数进行类型转换，以使其与另一个操作数兼容。
#### 引用[.reference_title]
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TypeError: unsupported operand type(s) for -: 'Tensor' and 'torch.return_types.max'

这个错误通常是因为你在进行张量计算时，使用了一个包含了特殊类型 `torch.return_types.max` 的张量。请检查你的代码中是否有这样的情况发生，尤其是在使用 `torch.max()` 函数时。

可能的解决方法是，使用 `.item()` 方法将 `torch.return_types.max` 转换为普通的 Python 整数，然后再进行计算。例如：

max_value, max_index = torch.max(tensor, dim=1)
max_value = max_value.item()  # 将 torch.return_types.max 转换为 Python 整数
result = tensor - max_value

如果你的问题不是这个，请提供更多的上下文，这样我才能更好地帮助你。