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

    【Schema】
    【DB_ID】 college_completion
【Schema】
# Table: institution_details
[
(unitid:INTEGER, Primary Key, Examples: [100654, 100663, 100690]),
(chronname:TEXT, Examples: [Alabama A&M University]),
(city:TEXT, Examples: [Normal, Birmingham, Montgomery]),
(state:TEXT, Examples: [Alabama, Alaska, Arizona]),
(level:TEXT, Examples: [4-year, 2-year]),
(control:TEXT, Examples: [Public]),
(basic:TEXT, Examples: [Masters Colleges and Universities--larger programs]),
(hbcu:TEXT, Examples: [X, NULL]),
(flagship:TEXT, Examples: [NULL, X]),
(long_x:REAL, Examples: [-86.568502, -86.80917, -86.17401]),
(lat_y:REAL, Examples: [34.783368, 33.50223, 32.362609]),
(site:TEXT, Examples: [www.aamu.edu/]),
(student_count:INTEGER, Examples: [4051, 11502, 322]),
(awards_per_value:REAL, Examples: [14.2, 20.9, 29.9]),
(awards_per_state_value:REAL, Examples: [18.8, 17.8, 15.9]),
(awards_per_natl_value:REAL, Examples: [21.5, 22.5, 16.5]),
(exp_award_value:INTEGER, Examples: [105331, 136546, 58414]),
(exp_award_state_value:INTEGER, Examples: [75743, 92268, 42194]),
(exp_award_natl_value:INTEGER, Examples: [66436, 101725, 37780]),
(exp_award_percentile:INTEGER, Examples: [90, 97, 30]),
(ft_pct:REAL, Examples: [93.8, 72.7, 62.7]),
(fte_value:INTEGER, Examples: [3906, 10032, 294]),
(fte_percentile:INTEGER, Examples: [33, 67, 12]),
(med_sat_value:TEXT, Examples: [823, 1146, NULL]),
(med_sat_percentile:TEXT, Examples: [0, 84, NULL]),
(aid_value:INTEGER, Examples: [7142, 6088, 2540]),
(aid_percentile:INTEGER, Examples: [72, 50, 1]),
(endow_value:TEXT, Examples: [NULL, 24136, 302]),
(endow_percentile:TEXT, Examples: [NULL, 93, 1]),
(grad_100_value:REAL, Examples: [10.0, 29.4, 0.0]),
(grad_100_percentile:INTEGER, Examples: [15, 67, 0]),
(grad_150_value:REAL, Examples: [29.1, 53.5, 66.7]),
(grad_150_percentile:INTEGER, Examples: [14, 66, 72]),
(pell_value:REAL, Examples: [71.2, 35.1, 68.4]),
(pell_percentile:INTEGER, Examples: [98, 39, 91]),
(retain_value:REAL, Examples: [63.1, 80.2, 37.5]),
(retain_percentile:INTEGER, Examples: [17, 70, 2]),
(ft_fac_value:REAL, Examples: [82.8, 92.4, 67.2]),
(ft_fac_percentile:INTEGER, Examples: [89, 98, 71]),
(vsa_year:TEXT, Examples: [2010, 2011, NULL]),
(vsa_grad_after4_first:TEXT, Examples: [14.7, 22.3, NULL]),
(vsa_grad_elsewhere_after4_first:TEXT, Examples: [2, 2.9, NULL]),
(vsa_enroll_after4_first:TEXT, Examples: [36.5, 34.2, NULL]),
(vsa_enroll_elsewhere_after4_first:TEXT, Examples: [16.1, 19.2, NULL]),
(vsa_grad_after6_first:TEXT, Examples: [33, 42.6, NULL]),
(vsa_grad_elsewhere_after6_first:TEXT, Examples: [5.3, 10.5, NULL]),
(vsa_enroll_after6_first:TEXT, Examples: [12.5, 7.9, NULL]),
(vsa_enroll_elsewhere_after6_first:TEXT, Examples: [14.6, 13.1, NULL]),
(vsa_grad_after4_transfer:TEXT, Examples: [15.7, NULL, 0]),
(vsa_grad_elsewhere_after4_transfer:TEXT, Examples: [1.5, NULL, 0]),
(vsa_enroll_after4_transfer:TEXT, Examples: [40.9, NULL, 0]),
(vsa_enroll_elsewhere_after4_transfer:TEXT, Examples: [17.2, NULL, 0]),
(vsa_grad_after6_transfer:TEXT, Examples: [36.4, NULL, 0]),
(vsa_grad_elsewhere_after6_transfer:TEXT, Examples: [5.6, NULL, 0]),
(vsa_enroll_after6_transfer:TEXT, Examples: [17.2, NULL, 0]),
(vsa_enroll_elsewhere_after6_transfer:TEXT, Examples: [11.1, NULL, 0]),
(similar:TEXT),
(state_sector_ct:INTEGER, Examples: [13, 16, 25]),
(carnegie_ct:INTEGER, Examples: [386, 106, 252]),
(counted_pct:TEXT, Examples: [99.7|07, 56.0|07, 100.0|07]),
(nicknames:TEXT, Examples: [NULL, UAB, UAH]),
(cohort_size:INTEGER, Examples: [882, 1376, 3])
]
# Table: state_sector_grads
[
(stateid:INTEGER, Examples: [1, 2, 4]),
(state:TEXT, Examples: [Alabama, Alaska, Arizona]),
(state_abbr:TEXT, Examples: [AL, AK, AZ]),
(control:TEXT, Examples: [Private for-profit]),
(level:TEXT, Examples: [4-year, 2-year]),
(year:INTEGER, Examples: [2011, 2012, 2013]),
(gender:TEXT, Examples: [B, F, M]),
(race:TEXT, Examples: [A, Ai, B]),
(cohort:TEXT, Examples: [4y bach, 4y other, 2y all]),
(grad_cohort:TEXT, Examples: [0, 1, 51]),
(grad_100:TEXT, Examples: [0, 2, 15]),
(grad_150:TEXT, Examples: [0, 3, 18]),
(grad_100_rate:TEXT, Examples: [NULL, 0, 3.9]),
(grad_150_rate:TEXT, Examples: [NULL, 0, 5.9]),
(grad_cohort_ct:INTEGER, Examples: [9, 16, 13])
]
# Table: institution_grads
[
(unitid:INTEGER, Examples: [100760, 101028, 101143]),
(year:INTEGER, Examples: [2011, 2012, 2013]),
(gender:TEXT, Examples: [B, M, F]),
(race:TEXT, Examples: [X, W, B]),
(cohort:TEXT, Examples: [2y all, 4y other, 4y bach]),
(grad_cohort:TEXT, Examples: [446, 185, 261]),
(grad_100:TEXT, Examples: [73, NULL, 40]),
(grad_150:TEXT, Examples: [105, 40, 65]),
(grad_100_rate:TEXT, Examples: [16.4, NULL, 6.7]),
(grad_150_rate:TEXT, Examples: [23.5, 21.6, 24.9])
]
# Table: state_sector_details
[
(stateid:INTEGER, Primary Key, Examples: [0, 1, 2]),
(state:TEXT, Examples: [United States, Alabama, Alaska]),
(state_post:TEXT, Examples: [U.S., Ala., Alaska]),
(level:TEXT, Primary Key, Examples: [2-year, 4-year]),
(control:TEXT, Primary Key, Examples: [Private for-profit]),
(schools_count:INTEGER, Examples: [632, 1180, 527]),
(counted_pct:TEXT, Examples: [NULL, 61, 62]),
(awards_per_state_value:TEXT, Examples: [NULL, 18.8, 17.8]),
(awards_per_natl_value:REAL, Examples: [21.5, 22.5, 24.6]),
(exp_award_state_value:TEXT, Examples: [NULL, 75743, 92268]),
(exp_award_natl_value:INTEGER, Examples: [66436, 101725, 38763]),
(state_appr_value:TEXT, Examples: [NULL, 290.68, 496.78]),
(state_appr_rank:TEXT, Examples: [NULL, 11, 2]),
(grad_rate_rank:TEXT, Examples: [23, 18, 8]),
(awards_per_rank:TEXT, Examples: [NULL, 42, 49])
]
【Foreign keys】
state_sector_grads.state=institution_details.state
state_sector_grads.stateid=state_sector_details.stateid
institution_grads.unitid=institution_details.unitid
state_sector_details.state=institution_details.state

    【Question】
    What is the total male graduates in 2012 in the state whereby the institute with the highest average amount of student aid going to undergraduate recipients is located? male refers to gender = 'M'; graduates refers to grad_cohort; in 2012 refers to year = 2012; highest average amount of student aid going to undergraduate recipients refers to MAX(aid_value);

    【Evidence】
    male refers to gender = 'M'; graduates refers to grad_cohort; in 2012 refers to year = 2012; highest average amount of student aid going to undergraduate recipients refers to MAX(aid_value);

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

SQL: SELECT COUNT(T2.grad_cohort) FROM institution_details AS T1 INNER JOIN state_sector_grads AS T2 ON T1.state = T2.state WHERE T2.year = 2012 AND T2.gender = 'M' ORDER BY T1.aid_value DESC LIMIT 1