The code

The code defines a function named xxx that takes two parameters: the number of rows to generate and an instance of the Luigi task. The function first checks the parameter types, then runs the task to generate data, reads the generated data, filters the data based on age, and returns the filtered data.

The type of code 2024-12-16 12:17:52 27 views

OAuthlib requests_oauthlib

This function creates an OAuth2 authentication session using the OAuthlib library. It accepts client ID, client secret, and token URL as parameters and returns an instance of an OAuth2 session.

OAuth 2 authentication session creation 2024-12-16 12:17:51 31 views

This code

This code defines a function named xxx that uses the Marshmallow library to randomly create a Schema with a randomly selected field type. The function returns an instance of the Schema or a validation error.

The type of code 2024-12-16 12:17:49 34 views

graypy

This function randomly selects and returns a GELF (Graylog Extended Log Format) handler from the Graylog third-party library. GELF is a binary log format optimized for log transmission and storage. The function uses `random.choice` to randomly select between GELFHandler and GelfUdpHandler, then creates an instance of the corresponding handler based on the choice.

The type of code 2024-12-16 12:17:37 22 views

MongoDB Motor

This function is used to randomly retrieve a document from a specified collection in a MongoDB database. It connects to a local MongoDB instance, specifies the database and collection, and then queries the first document with a string type ID.

Database operation function 2024-12-16 12:17:10 16 views

Authlib OAuth2Provider

Create an OAuth2Provider instance for handling the OAuth2 authentication process. It requires a client ID and client secret.

Function 2024-12-16 12:16:42 12 views

Allennlp Predictor

This function randomly selects a predictor from the Allennlp library and uses a randomly generated text for prediction.

Function 2024-12-16 12:16:42 19 views

Allennlp

This code defines a simple sentiment analysis model using components from the Allennlp library. It first defines a tokenizer, vocabulary, text field, label field, embedder, feedforward network, and regularizer. Then it creates an instance and trains a model. Finally, it defines a function that uses the model to predict the sentiment of an input sentence.

Text classification model 2024-12-16 12:16:37 17 views

Allennlp Vocabulary

This function uses the Allennlp library to predict the label of a given text. It first loads a pre-trained model and vocabulary. Then, it splits the input text into words and finds the index of each word in the vocabulary. Next, it creates an instance and passes it to the model for prediction. Finally, it returns the predicted label.

The type of code 2024-12-16 12:16:22 18 views

CatBoost NumPy

This function uses a CatBoost model to predict the output for a given data. It first checks if the input model is an instance of CatBoostClassifier, and then makes predictions using the model.

Function 2024-12-16 12:16:06 13 views