input: Write a python function to load a CSV file, split it into features and target, train a linear regression model, and return the model's coefficients.
  assert model_coefficients('data.csv') == {'Intercept': <expected_intercept>, 'Coefficients': <expected_coefficients>}
  assert model_coefficients('another_data.csv') == {'Intercept': <expected_intercept>, 'Coefficients': <expected_coefficients>}
  assert model_coefficients('yet_another_data.csv') == {'Intercept': <expected_intercept>, 'Coefficients': <expected_coefficients>}
output: def model_coefficients(file_path):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
      
      # Load the CSV file into a DataFrame
      data = pd.read_csv(file_path)
      
      # Split the DataFrame into features and target
      X = data.drop(columns='target')
      y = data['target']
      
      # Train a linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Get the model's coefficients and intercept
      intercept = model.intercept_
      coefficients = model.coef_
      
      return {'Intercept': intercept, 'Coefficients': coefficients.tolist()}