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RNN (Recurrent Neural Network) is a type of neural network that is designed to process sequential data such as time-series data or natural language text. PyTorch is a popular deep learning framework that provides easy-to-use tools for building and training RNN models.

In PyTorch, you can define an RNN model using the `nn.RNN` module. The `nn.RNN` module takes in the input sequence and the initial hidden state as inputs, and returns the output sequence and the final hidden state as outputs.

Here is an example of how to define an RNN model in PyTorch:

import torch
import torch.nn as nn

# Define the RNN model
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        # Initialize the hidden state
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)

        # Forward pass through the RNN layer
        out, h = self.rnn(x, h0)

        # Pass the output through the fully-connected layer
        out = self.fc(out[:, -1, :])

        return out

In this example, we define an RNN model with one RNN layer, one fully-connected layer, and a hidden state size of 128. The `input_size` is the size of the input feature vector, `num_layers` is the number of RNN layers, and `output_size` is the size of the output vector.

In the `forward` method, we initialize the hidden state to all zeros, pass the input sequence `x` through the RNN layer, and then pass the final output through the fully-connected layer to obtain the final output.

To train this model, you can use the standard PyTorch training loop with a loss function such as mean squared error (MSE) or cross-entropy loss, and an optimizer such as stochastic gradient descent (SGD) or Adam.