请你学习pytorch相关应用教程，给出一个以pytorch实现的具有LSTM循环神经网络（LSTM-RNN） 的例子， 解释代码和运行结果。

在PyTorch库中，LSTM (长短期记忆) 网络是一种常用的循环神经网络，特别适合处理序列数据，如文本、音频或时间序列预测。下面是一个简单的例子，我们将构建一个LSTM用于文本生成：

import torch
import torch.nn as nn
from torchtext.data import Field, BucketIterator

# 初始化超参数
input_dim = 50  # 假设输入特征维度为50
hidden_dim = 256
num_layers = 2
batch_size = 64
seq_length = 10  # 序列长度
output_dim = 10  # 输出类别数（例如，单词表大小）

# 定义词汇场和创建数据加载器
TEXT = Field(tokenize='spacy', init_token='<sos>', eos_token='<eos>', lower=True)
train_data, valid_data, test_data = TEXT.ngrams('your_text_dataset.txt', n=seq_length)

TEXT.build_vocab(train_data, min_freq=2)

# 创建模型
class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(num_layers, x.size(0), self.hidden_dim).to(device)
        c0 = torch.zeros(num_layers, x.size(0), self.hidden_dim).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTMModel(input_dim, hidden_dim, output_dim, num_layers).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练过程
for epoch in range(10):  # 假设我们训练10轮
    for batch in train_loader:
        inputs, targets = batch.text.to(device), batch.label.to(device)
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

# 使用模型预测
sample_sequence = TEXT.ngrams("示例文本", seq_length)[0][0].numpy().tolist()
sequence = [TEXT.vocab.stoi[sample] for s in sample_sequence]

with torch.no_grad():
    for i in range(100):
        x = torch.tensor([sequence[-seq_length:]], device=device)
        pred = model(x)
        next_word_idx = pred.argmax(1).item()
        sequence.append(next_word_idx)
        sequence = sequence[-seq_length:]

print("生成的文本：", ''.join(TEXT.itos[word_idx] for word_idx in sequence))