input: Write a python function to group a DataFrame by a specified column and calculate the sum of one column and the mean of another.
  ```python
  import pandas as pd
  
  def group_by_column(df, group_col, sum_col, mean_col):
      return df.groupby(group_col).agg({sum_col: 'sum', mean_col: 'mean'})
  
  # Test cases
  df1 = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": ["x", "y", "x", "y", "x"], "C": [10, 20, 30, 40, 50], "D": [1.1, 2.2, 3.3, 4.4, 5.5]})
  assert group_by_column(df1, "B", "C", "D").equals(pd.DataFrame({"C": [90, 60], "D": [3.3, 3.3]}, index=["x", "y"]))
  
  df2 = pd.DataFrame({"B": ["a", "b", "a", "b", "a"], "C": [5, 10, 15, 20, 25], "D": [0.5, 1.0, 1.5, 2.0, 2.5]})
  assert group_by_column(df2, "B", "C", "D").equals(pd.DataFrame({"C": [45, 30], "D": [1.5, 1.5]}, index=["a", "b"]))
output: def group_by_column(df, group_col, sum_col, mean_col):
      return df.groupby(group_col).agg({sum_col: 'sum', mean_col: 'mean'})