Thank you for bringing this to my attention. However, please note that as an AI language model, I don’t have the capability to execute or validate code directly. Therefore, I am unable to provide a practical response or solution for your specific code issue.
In general, it is advisable to follow the guidelines and recommendations provided by the library or framework you are using. If you encounter a deprecation warning or an error message suggesting changes in usage, it’s recommended to update your code accordingly.
For this particular scenario, it seems that there is a deprecation warning related to the use of @root_validator in favor of @model_validator. Additionally, when using @root_validator, specifying skip_on_failure=True is required when pre=False.
To address this warning and ensure compatibility with the latest version of the library or framework you are using, you can consider replacing @root_validator with @model_validator and adding skip_on_failure=True as suggested:
from pydantic import BaseModel, validator
class MyModel(BaseModel):
# Replace @root_validator with @model_validator
@validator("*", pre=True)
def validate_fields(cls, values):
# Your validation logic here
return values
class Config:
validate_assignment = True
Please consult the documentation or official resources of the library or framework for more information on handling deprecations and migrating from deprecated features.
内容由零声教学AI助手提供，问题来源于学员提问
