input: Write a python function to load a CSV file and train a linear regression model to predict sales based on input features.
  assert train_model('sales_data.csv').coef_.shape == (5,)
  assert train_model('other_sales_data.csv').intercept_ is not None
output: def train_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      
      # Load the CSV file into a DataFrame
      data = pd.read_csv(file_path)
      
      # Assuming the target variable is 'sales' and all other columns are features
      X = data.drop('sales', axis=1)
      y = data['sales']
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create and train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      return model