input: Write a python function to load a CSV file, remove duplicates, convert to numeric where possible, and assert the function with the following test cases.
  assert optimize_data('test1.csv').equals(pd.DataFrame({'A': [1, 2], 'B': [3, 4]}))
  assert optimize_data('test2.csv').equals(pd.DataFrame({'A': [5, 6], 'B': [7, 8]}))
  assert optimize_data('test3.csv').equals(pd.DataFrame({'A': [9, 10], 'B': [11, 12]}))
output: import pandas as pd
  
  def optimize_data(file_path):
      # Load CSV file into a DataFrame
      df = pd.read_csv(file_path)
      
      # Remove duplicate rows
      df = df.drop_duplicates()
      
      # Convert columns to numeric values where possible
      df = df.apply(pd.to_numeric, errors='coerce')
      
      # Drop rows with NaN values
      df = df.dropna()
      
      return df