input: Write a function to create a PyTorch neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The function should return the model and the predicted price for the next day given an input sequence of prices.
  assert create_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])[1] >= 11.0
  assert create_model([100, 101, 102, 103, 104, 105, 106])[1] >= 106.0
  assert create_model([50, 51, 52, 53, 54, 55, 56])[1] >= 56.0
  assert create_model([20, 21, 22, 23, 24, 25, 26])[1] >= 26.0
  assert create_model([10.4, 10.5, 10.6, 10.7, 10.9, 11, 11.1])[1] >= 11.1
output: import torch
  import torch.nn as nn
  
  def create_model(prices):
      class StockPredictor(nn.Module):
          def __init__(self):
              super(StockPredictor, self).__init__()
              self.fc1 = nn.Linear(6, 1)
          
          def forward(self, x):
              return self.fc1(x)
      
      model = StockPredictor()
      input_seq = torch.tensor(prices[:-1]).float().unsqueeze(0)
      predicted_price = model(input_seq).item()
      return model, predicted_price