input: Write a python function to predict temperatures for the next three days using polynomial regression. 
  assert len(predict_next_three_days('dataset.csv')) == 3
  assert isinstance(predict_next_three_days('dataset.csv')[0], float)
  assert predict_next_three_days('dataset.csv')[0] > -50 # assuming temperature in a reasonable range
output: def predict_next_three_days(csv_file):
      import pandas as pd
      from sklearn.preprocessing import PolynomialFeatures
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Load the dataset
      df = pd.read_csv(csv_file)
  
      # Ensure the dataset has the required columns
      if 'Day' not in df.columns or 'Temperature' not in df.columns:
          raise ValueError("Dataset must contain 'Day' and 'Temperature' columns")
  
      # Extract features and target variable
      X = df[['Day']].values
      y = df['Temperature'].values
  
      # Polynomial regression
      poly = PolynomialFeatures(degree=2)
      X_poly = poly.fit_transform(X)
      
      model = LinearRegression()
      model.fit(X_poly, y)
  
      # Predict temperatures for the next three days
      last_day = df['Day'].max()
      next_days = np.array([[last_day + 1], [last_day + 2], [last_day + 3]])
      next_days_poly = poly.transform(next_days)
      
      predictions = model.predict(next_days_poly)
      
      return predictions.tolist()