填写以下代码，并给出详细解释Hypothesis = namedtuple('Hypothesis', ['value', 'score']) class NMT(nn.Module): """ Simple Neural Machine Translation Model: - Bidrectional LSTM Encoder - Unidirection LSTM Decoder - Global Attention Model (Luon

The code you provided defines a named tuple `Hypothesis` with two fields, `value` and `score`. This is a convenient way to store and manipulate hypotheses in the context of sequence-to-sequence models.

The `NMT` class is a PyTorch module that implements a simple neural machine translation model. It consists of a bidirectional LSTM encoder, a unidirectional LSTM decoder, and a global attention mechanism based on Luong et al. (2015).

Here's a breakdown of the code:

from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F

Hypothesis = namedtuple('Hypothesis', ['value', 'score'])

class NMT(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, emb_size, hidden_size):
        super(NMT, self).__init__()
        self.src_embed = nn.Embedding(src_vocab_size, emb_size)
        self.tgt_embed = nn.Embedding(tgt_vocab_size, emb_size)
        self.encoder = nn.LSTM(emb_size, hidden_size, bidirectional=True)
        self.decoder = nn.LSTMCell(emb_size + hidden_size, hidden_size)
        self.attention = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, tgt_vocab_size)
        self.hidden_size = hidden_size

    def forward(self, src, tgt):
        batch_size = src.size(0)
        src_len = src.size(1)
        tgt_len = tgt.size(1)

        # Encode the source sentence
        src_embedded = self.src_embed(src)
        encoder_outputs, (last_hidden, last_cell) = self.encoder(src_embedded)

        # Initialize the decoder states
        decoder_hidden = last_hidden.view(batch_size, self.hidden_size)
        decoder_cell = last_cell.view(batch_size, self.hidden_size)

        # Initialize the attention context vector
        context = torch.zeros(batch_size, self.hidden_size, device=src.device)

        # Initialize the output scores
        outputs = torch.zeros(batch_size, tgt_len, self.hidden_size, device=src.device)

        # Decode the target sentence
        for t in range(tgt_len):
            tgt_embedded = self.tgt_embed(tgt[:, t])
            decoder_input = torch.cat([tgt_embedded, context], dim=1)
            decoder_hidden, decoder_cell = self.decoder(decoder_input, (decoder_hidden, decoder_cell))
            attention_scores = self.attention(encoder_outputs)
            attention_weights = F.softmax(torch.bmm(attention_scores, decoder_hidden.unsqueeze(2)).squeeze(2), dim=1)
            context = torch.bmm(attention_weights.unsqueeze(1), encoder_outputs).squeeze(1)
            output = self.out(decoder_hidden)
            outputs[:, t] = output

        return outputs

The `__init__` method initializes the model parameters and layers. It takes four arguments:

- `src_vocab_size`: the size of the source vocabulary
- `tgt_vocab_size`: the size of the target vocabulary
- `emb_size`: the size of the word embeddings
- `hidden_size`: the size of the encoder and decoder hidden states

The model has four main components:

- `src_embed`: an embedding layer for the source sentence
- `tgt_embed`: an embedding layer for the target sentence
- `encoder`: a bidirectional LSTM encoder that encodes the source sentence
- `decoder`: a unidirectional LSTM decoder that generates the target sentence

The attention mechanism is implemented in the `forward` method. It takes two arguments:

- `src`: the source sentence tensor of shape `(batch_size, src_len)`
- `tgt`: the target sentence tensor of shape `(batch_size, tgt_len)`

The method first encodes the source sentence using the bidirectional LSTM encoder. The encoder outputs and final hidden and cell states are stored in `encoder_outputs`, `last_hidden`, and `last_cell`, respectively.

The decoder is initialized with the final hidden and cell states of the encoder. At each time step, the decoder takes as input the embedded target word and the context vector, which is a weighted sum of the encoder outputs based on the attention scores. The decoder output and hidden and cell states are updated using the LSTMCell module.

The attention scores are calculated by applying a linear transform to the concatenated decoder hidden state and encoder outputs, followed by a softmax activation. The attention weights are used to compute the context vector as a weighted sum of the encoder outputs.

Finally, the decoder hidden state is passed through a linear layer to produce the output scores for each target word in the sequence. The output scores are stored in the `outputs` tensor and returned by the method.