# # few_shot implementation
# import torch
# from corerec.train import train_model
# from corerec.predict import predict

# """
# FewShotLearner class implements a few-shot learning paradigm for training and prediction.

# Few-shot learning is a machine learning approach where the model is trained to learn information
# about a task from a very small amount of data. This class leverages a pre-defined model and
# data loader to perform few-shot learning.

# Attributes:
#     model (torch.nn.Module): The neural network model to be trained and used for predictions.
#     data_loader (torch.utils.data.DataLoader): DataLoader providing the training data.
#     criterion (torch.nn.Module): Loss function used to evaluate the model's performance.
#     optimizer (torch.optim.Optimizer): Optimization algorithm used to update model weights.
#     num_epochs (int): Number of epochs to train the model.

# Methods:
#     train(): Trains the model using the few-shot learning approach.
#     predict(graph, node_index, top_k=5, threshold=0.5): Predicts the top-k items for a given node in a graph.
# """

# class FewShotLearner:
#     def __init__(self, model, data_loader, criterion, optimizer, num_epochs):
#         """
#         Initializes the FewShotLearner with the given model, data loader, criterion, optimizer, and number of epochs.

#         Parameters:
#             model (torch.nn.Module): The model to be trained and used for predictions.
#             data_loader (torch.utils.data.DataLoader): DataLoader providing the training data.
#             criterion (torch.nn.Module): Loss function used to evaluate the model's performance.
#             optimizer (torch.optim.Optimizer): Optimization algorithm used to update model weights.
#             num_epochs (int): Number of epochs to train the model.
#         """
#         self.model = model
#         self.data_loader = data_loader
#         self.criterion = criterion
#         self.optimizer = optimizer
#         self.num_epochs = num_epochs

#     def train(self):
#         """
#         Trains the model using the few-shot learning approach.

#         This method iterates over the data provided by the data_loader for a specified number of epochs,
#         updating the model's weights using the optimizer and evaluating its performance using the criterion.
#         """
#         print("Training with Few-Shot Learning...")
#         train_model(self.model, self.data_loader, self.criterion, self.optimizer, self.num_epochs)

#     def predict(self, graph, node_index, top_k=5, threshold=0.5):
#         """
#         Predicts the top-k items for a given node in a graph using the trained model.

#         Parameters:
#             graph (torch.Tensor): The graph data structure containing nodes and edges.
#             node_index (int): The index of the node for which predictions are to be made.
#             top_k (int, optional): The number of top items to return. Defaults to 5.
#             threshold (float, optional): The threshold for prediction confidence. Defaults to 0.5.

#         Returns:
#             List[int]: A list of indices representing the top-k predicted items.
#         """
#         print("Predicting with Few-Shot Learning...")
#         return predict(self.model, graph, node_index, top_k, threshold)
