input: Write a function to identify all duplicates in a specified column of a Pandas DataFrame and return the rows containing duplicates.
  assert identify_duplicates(pd.DataFrame({'Name': ['John', 'James', 'John'], 'Age': [27, 25, 27], 'City': ['London', 'Tokyo', 'Tokyo']}), 'Name').equals(pd.DataFrame({'Name': ['John', 'John'], 'Age': [27, 27], 'City': ['London', 'Tokyo']}))
  assert identify_duplicates(pd.DataFrame({'Name': ['Anna', 'Anna', 'Bob'], 'Age': [30, 30, 22], 'City': ['Paris', 'Paris', 'Berlin']}), 'Name').equals(pd.DataFrame({'Name': ['Anna', 'Anna'], 'Age': [30, 30], 'City': ['Paris', 'Paris']}))
  assert identify_duplicates(pd.DataFrame({'Product': ['Apple', 'Banana', 'Apple', 'Cherry'], 'Price': [1.2, 0.5, 1.2, 2.0]}), 'Product').equals(pd.DataFrame({'Product': ['Apple', 'Apple'], 'Price': [1.2, 1.2]}))
output: import pandas as pd
  
  def identify_duplicates(df, column_name):
      return df[df[column_name].duplicated(keep=False)]