You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 address
【Schema】
# Table: country
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(county:TEXT, Primary Key, Examples: [SUFFOLK, ADJUNTAS, AGUADA]),
(state:TEXT, Examples: [NY, PR, VI])
]
# Table: CBSA
[
(CBSA:INTEGER, Primary Key, Examples: [10300, 10380, 10420]),
(CBSA_name:TEXT, Examples: [Adrian, MI]),
(CBSA_type:TEXT, Examples: [Micro, Metro])
]
# Table: zip_congress
[
(zip_code:INTEGER, Primary Key, Examples: [501, 601, 602]),
(district:TEXT, Primary Key, Examples: [NY-1, PR, VI])
]
# Table: congress
[
(cognress_rep_id:TEXT, Primary Key, Examples: [AK, AK-S1, AK-S2]),
(first_name:TEXT, Examples: [Young, Begich, Murkowski]),
(last_name:TEXT, Examples: [Don, Mark, Lisa]),
(CID:TEXT, Examples: [N00008091, N00009585, N00033101]),
(party:TEXT, Examples: [Republican, Democrat, Independent]),
(state:TEXT, Examples: [Alaska, Alabama, Arkansas]),
(abbreviation:TEXT, Examples: [AK, AL, AR]),
(House:TEXT, Examples: [House of Repsentatives]),
(District:INTEGER, Examples: [1, 2, 3]),
(land_area:REAL, Examples: [571951.26, 570641.0, 6066.83])
]
# Table: avoid
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(bad_alias:TEXT, Primary Key, Examples: [Internal Revenue Service])
]
# Table: area_code
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(area_code:INTEGER, Primary Key, Examples: [631, 787, 939])
]
# Table: alias
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(alias:TEXT, Examples: [Holtsville, Adjuntas, Aguada])
]
# Table: state
[
(abbreviation:TEXT, Primary Key, Examples: [AA, AE, AK]),
(name:TEXT, Examples: [Armed Forces Americas])
]
# Table: zip_data
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(city:TEXT, Examples: [Holtsville, Adjuntas, Aguada]),
(state:TEXT, Examples: [NY, PR, VI]),
(multi_county:TEXT, Examples: [No, Yes]),
(type:TEXT, Examples: [Unique Post Office, Post Office, P.O. Box Only]),
(organization:TEXT, Examples: [I R S Service Center]),
(time_zone:TEXT, Examples: [Eastern, Atlantic, Central]),
(daylight_savings:TEXT, Examples: [Yes, No]),
(latitude:REAL, Examples: [40.817923, 40.788827, 18.196747]),
(longitude:REAL, Examples: [-73.045317, -73.039405, -66.736735]),
(elevation:INTEGER, Examples: [25, 0, 70]),
(state_fips:INTEGER, Examples: [36, 72, 78]),
(county_fips:INTEGER, Examples: [103, 1, 3]),
(region:TEXT, Examples: [Northeast, South, Midwest]),
(division:TEXT, Examples: [Middle Atlantic, New England, South Atlantic]),
(population_2020:INTEGER, Examples: [0, 11737, 24263]),
(population_2010:INTEGER, Examples: [0, 18570, 41520]),
(households:INTEGER, Examples: [0, 6525, 15002]),
(avg_house_value:INTEGER, Examples: [0, 86200, 86300]),
(avg_income_per_household:INTEGER, Examples: [0, 13092, 16358]),
(persons_per_household:REAL, Examples: [0.0, 2.84, 2.76]),
(white_population:INTEGER, Examples: [0, 17479, 36828]),
(black_population:INTEGER, Examples: [0, 663, 2860]),
(hispanic_population:INTEGER, Examples: [0, 18486, 41265]),
(asian_population:INTEGER, Examples: [0, 7, 42]),
(american_indian_population:INTEGER, Examples: [0, 113, 291]),
(hawaiian_population:INTEGER, Examples: [0, 10, 32]),
(other_population:INTEGER, Examples: [0, 558, 2634]),
(male_population:INTEGER, Examples: [0, 9078, 20396]),
(female_population:INTEGER, Examples: [0, 9492, 21124]),
(median_age:REAL, Examples: [0.0, 35.9, 37.5]),
(male_median_age:REAL, Examples: [0.0, 34.5, 36.6]),
(female_median_age:REAL, Examples: [0.0, 37.1, 38.5]),
(residential_mailboxes:INTEGER, Examples: [0, 4133, 8791]),
(business_mailboxes:INTEGER, Examples: [1, 0, 221]),
(total_delivery_receptacles:INTEGER, Examples: [1, 0, 5173]),
(businesses:INTEGER, Examples: [2, 0, 472]),
(1st_quarter_payroll:INTEGER, Examples: [0, 88685000, 109167000]),
(annual_payroll:INTEGER, Examples: [0, 376492000, 472633000]),
(employees:INTEGER, Examples: [0, 9127, 18894]),
(water_area:REAL, Examples: [0.0, 0.309, 1.71]),
(land_area:REAL, Examples: [0.0, 64.348, 30.621]),
(single_family_delivery_units:INTEGER, Examples: [0, 2419, 5473]),
(multi_family_delivery_units:INTEGER, Examples: [0, 1264, 827]),
(total_beneficiaries:INTEGER, Examples: [0, 5135, 16805]),
(retired_workers:INTEGER, Examples: [0, 2940, 12145]),
(disabled_workers:INTEGER, Examples: [0, 885, 1835]),
(parents_and_widowed:INTEGER, Examples: [0, 155, 1045]),
(spouses:INTEGER, Examples: [0, 145, 1085]),
(children:INTEGER, Examples: [0, 1010, 695]),
(over_65:INTEGER, Examples: [0, 3245, 13260]),
(monthly_benefits_all:INTEGER, Examples: [0, 6137000, 14602000]),
(monthly_benefits_retired_workers:INTEGER, Examples: [0, 4007000, 10947000]),
(monthly_benefits_widowed:INTEGER, Examples: [0, 185000, 966000]),
(CBSA:INTEGER, Examples: [35620, 38660, 10380])
]
【Foreign keys】
country.zip_code=zip_data.zip_code
country.state=state.abbreviation
zip_congress.district=congress.cognress_rep_id
zip_congress.zip_code=zip_data.zip_code
congress.abbreviation=state.abbreviation
avoid.zip_code=zip_data.zip_code
area_code.zip_code=zip_data.zip_code
alias.zip_code=zip_data.zip_code
zip_data.state=state.abbreviation
zip_data.CBSA=CBSA.CBSA

    【Question】
    What is the Asian population in the city with the alias Leeds?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(T2.asian_population) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Leeds'