import torch
import torch.nn as nn

# Example sequence: batch size 1, sequence length 5, input size 1
sequence = torch.tensor([[[1.0]], [[2.0]], [[3.0]], [[4.0]], [[5.0]]])

input_size = 1    # Input dimension at each step
hidden_size = 2   # Hidden state size

rnn = nn.RNN(input_size, hidden_size, batch_first=False)

# Initialize hidden state: (num_layers, batch_size, hidden_size)
h_t = torch.zeros(1, 1, hidden_size)

print("Step | Input | Hidden State")
print("-------------------------")

for i, x_t in enumerate(sequence):
    out, h_t = rnn(x_t.unsqueeze(0), h_t)  # Add batch/time axes
    print(f"  {i+1}  |  {x_t.item()}   |  {h_t.squeeze(0).detach().numpy()}")