get_list_or_404

是的，`get_list_or_404()`函数是Django框架中的一个函数。它跟`get_object_or_404()`类似，但是返回的是一个对象列表，而不是单个对象。它的作用是在数据库中查找一组对象，如果找到则返回该对象列表，否则会抛出一个404异常。通常在视图函数中使用它来获取一组特定的对象，比如从数据库中获取一组文章列表。如果该对象列表不存在，则会返回一个404错误页面，这样可以避免出现程序错误。使用方法如下：

from django.shortcuts import get_list_or_404
from myapp.models import MyModel

def my_view(request):
    my_objects = get_list_or_404(MyModel, published=True)
    # Do something with my_objects

上面的代码会在数据库中查找`MyModel`模型中所有`published`属性为`True`的对象，如果找到则返回一个对象列表，否则会抛出一个404异常。

相关问题

pd.get_dummies

pd.get_dummies is a Python function from the pandas library that is used to create dummy variables from categorical data. It creates a new column for each unique category of a categorical variable, and assigns a value of 1 or 0 to each row depending on whether that row belongs to that category or not. This is useful for machine learning algorithms that require numerical input, as it converts non-numerical data into a numerical format.

For example, if we have a dataset with a categorical variable "color" that has three categories: red, green, and blue, pd.get_dummies will create three new columns in the dataset called "color_red", "color_green", and "color_blue". Each row will have a value of 1 in the column that corresponds to its color, and 0 in the other two columns.

The syntax for pd.get_dummies is:

pd.get_dummies(data, columns=None, prefix=None, prefix_sep='_', dummy_na=False, drop_first=False)

- data: the input pandas DataFrame or Series
- columns: the name or list of names of the columns to encode. If not specified, all non-numerical columns will be encoded.
- prefix: the prefix to add to the column names of the new dummy variables
- prefix_sep: the separator to use between the prefix and the original column name
- dummy_na: whether to create an additional column for missing values. If True, a column called "column_name_nan" will be created for each column with missing values.
- drop_first: whether to drop the first column of each set of dummy variables to avoid multicollinearity. If True, the first column will be dropped.

all_grads = get_or_compute_grads(loss_or_grads, params)报错

这个报错可能是因为 `loss_or_grads` 或者 `params` 这些参数没有正确的被定义。请确保 `loss_or_grads` 是一个标量张量（scalar tensor）或者梯度列表（gradient list），`params` 是一个变量列表（variable list）。如果传入的参数都正确，请检查它们的类型和形状是否正确。