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  "##characteristics#run#vqe_input#minimizer_options#maxfev#range_percent": 0.0,
  "##characteristics#run#vqe_input#minimizer_options#maxfev#repeats": 10,
  "##characteristics#run#vqe_input#minimizer_src#all": [
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n"
  ],
  "##characteristics#run#vqe_input#minimizer_src#all_unique": [
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n"
  ],
  "##characteristics#run#vqe_input#minimizer_src#min": "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n    global invocations\n    print(\"x0 is\", x0)\n    para_func = mapf(func)\n    invocations = 0\n    def wp(*test):\n        global invocations\n        args = test[0][0]\n        print(\"invoking with\", args)\n        ret = func(args, my_args)\n        print(\"evaluated as\", ret)\n        invocations += SAMPLE_SZ\n        return ret\n\n    #inital_pts = []\n    #inital_values = []\n    #for x in np.linspace(0, 2*np.pi, 3):\n    #    for y in np.linspace(0, 2*np.pi, 3):\n    #        for rpt in range(5):\n    #            ret = func([x, y], my_args)\n    #            inital_pts.append((x, y))\n    #            inital_values.append(ret)\n    #            invocations += SAMPLE_SZ\n    \n    myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n                                                acquisition_type = 'MPI',\n                                                initial_design_numdata = 25,\n                                                Initial_design_type=\"latin\",\n                                                evaluator_type = 'local_penalization',\n                                                num_cores=1, batch_size=1,\n                                                #X=np.array(inital_pts).transpose(),\n                                                #Y=np.array([inital_values]),\n                                                jitter=0.3)\n    myProblem.run_optimization(40)\n    myProblem.plot_acquisition()\n\n    current_func_value = func(myProblem.x_opt, my_args)\n    minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n    return minimizer_output\n",
  "##characteristics#run#vqe_input#minimizer_src#repeats": 10,
  "##characteristics#run#vqe_input#q_device_name#all": [
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    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
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    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts",
    "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx rdtscp lm constant_tsc nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti retpoline spec_ctrl fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid xsaveopt dtherm arat pln pts"
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}