用代码实现：def nn_forward_pass(params: Dict[str, torch.Tensor], X: torch.Tensor): """ The first stage of our neural network implementation: Run the forward pass of the network to compute the hidden layer features and classification scores. The network architecture should be: FC layer -> ReLU (hidden) -> FC layer (scores) As a practice, we will NOT allow to use torch.relu and torch.nn ops just for this time (you can use it from A3). Inputs: - params: a dictionary of PyTorch Tensor that store the weights of a model. It should have following keys with shape W1: First layer weights; has shape (D, H) b1: First layer biases; has shape (H,) W2: Second layer weights; has shape (H, C) b2: Second layer biases; has shape (C,) - X: Input data of shape (N, D). Each X[i] is a training sample. Returns a tuple of: - scores: Tensor of shape (N, C) giving the classification scores for X - hidden: Tensor of shape (N, H) giving the hidden layer representation for each input value (after the ReLU). """

import torch

def nn_forward_pass(params: Dict[str, torch.Tensor], X: torch.Tensor):
    # extract the weights and biases from the dictionary
    W1 = params['W1']
    b1 = params['b1']
    W2 = params['W2']
    b2 = params['b2']
    
    # compute the first layer activations
    hidden = torch.matmul(X, W1) + b1
    
    # apply the ReLU activation function
    hidden = torch.max(torch.zeros_like(hidden), hidden)
    
    # compute the scores
    scores = torch.matmul(hidden, W2) + b2
    
    return scores, hidden

这个函数实现了一个两层神经网络的前向传播，其中第一层是全连接层，第二层是输出层。对于每个输入值，神经网络首先计算第一层的激活，并将其输入到ReLU激活函数中。然后，它计算第二层的分类得分并返回结果。同时，它还返回第一层的激活表示，即隐藏层。