Challenger Montevideo stats & predictions

Juan Martín Del Potro

A seasoned player known for his powerful forehand and resilience on the court. Del Potro has consistently performed well in Challenger tournaments, making him a formidable opponent.

Federico Coria

A rising star in the tennis world, Coria brings speed and agility to his game. His recent performances have impressed many analysts, positioning him as a strong contender in this tournament.

Pablo Cuevas

As one of Uruguay's most celebrated tennis players, Cuevas brings both skill and national pride to his matches. His experience in high-pressure situations makes him a favorite among local fans.

Thiago Monteiro

An international competitor known for his strategic playstyle. Monteiro's ability to adapt quickly during matches makes him a challenging opponent for any player.

Alex De Miño

A young prodigy making his debut at this tournament. De Miño's natural talent and youthful energy have captured the attention of many tennis enthusiasts.

• Dress Appropriately: Given Uruguay's varying climate conditions throughout different months; ensure you pack clothes suitable for both warm days & chilly evenings!

• Purchase Tickets Early: Popular matches tend sell out quickly so securing your tickets ahead saves unnecessary stress later on down line!
  
  
  
  
• Eat Local: Explore traditional Uruguayan dishes such as "Asado" or "Empanadas" while enjoying live music performances near dining areas around stadiums offering great ambiance too!
  
  
• Safety First: Always remain vigilant about personal belongings when attending crowded events like these – keeping valuables secure ensures peace-of-mind throughout festivities!
  
• Catch Pre-Match Interviews:: Take advantage pre-match interviews featuring participating athletes – they provide valuable insights into upcoming games & strategies employed by competitors!
  
• Navigate Efficiently:: Familiarize yourself with stadium layout prior arrival helps avoid unnecessary delays finding seating areas/restrooms/bars etc., ensuring smooth enjoyment throughout day-long activities planned around venue grounds!
  
  
  
  Note: These suggestions aim at enhancing overall attendee experiences while respecting local customs & regulations within host country context – allowing seamless integration into vibrant cultural tapestry presented by city itself!
  
  *Always remember:* Enjoying sport should be fun-filled adventure prioritizing safety alongside camaraderie shared amongst fellow enthusiasts gathered together celebrating love affair played out across courts worldwide - including this prestigious event held annually right here right now back home where magic unfolds every single day!<|repo_name|>ravichandran-ravi/ai-assistant<|file_sep|>/kenya_kenyan_food_trends_and_influences.html

  Kenyan Food Trends: Exploring Influences Shaping Culinary Practices Today!

  Kenyan cuisine has evolved over time due largely influenced external factors such migration patterns global trade routes colonial rule etc., resulting fascinating fusion flavors found today’s dishes enjoyed across nation borders alike! In this article we delve deep into current food trends sweeping Kenya while examining key influences shaping culinary practices seen today – providing insight into how past meets present creating vibrant gastronomic landscape worth exploring further.

  The Rise Of Fusion Cuisine In Kenya: Blending Tradition And Modernity On Your Plate!

  In recent years there’s been noticeable shift towards embracing fusion cuisine within Kenyan culinary scene; where traditional recipes meet contemporary techniques yielding innovative dishes reflecting diverse cultural heritage present across country’s regions.

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  • Mukimo meets Mediterranean: Combining classic Kenyan staple ‘mukimo’ (a mash made from mashed potatoes mixed with beans) with Mediterranean ingredients like olive oil herbs creates refreshing twist traditional dish.
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  • Savanna BBQ: Incorporating Western-style barbecue methods alongside indigenous spices elevates simple grilled meats offering unique flavor profiles appreciated by locals tourists alike.
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  • Nairobi Street Food Innovation: Urban eateries experiment combining street food classics such us ‘nyama choma’ (grilled meat) or ‘ugali’ (cornmeal porridge) with exotic ingredients sourced globally expanding palate horizons.nn
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  Influence Of Migration On Kenyan Cuisine: A Melting Pot Of Flavors!nn

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  Migration patterns historically played significant role shaping Kenya’s culinary landscape; influx immigrants brought along distinct cooking styles enriching existing food traditions forming melting pot diverse flavors still celebrated today.

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  • Lusophone Influence: Portuguese settlers introduced spices like coriander cumin chili peppers influencing coastal cuisines particularly evident dishes such us ‘kuku paka’ (chicken cooked coconut milk curry).
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  • Middle Eastern Touches: Arab traders centuries ago left lasting impact seen incorporation elements such us pilau rice biryani biriani showcasing harmonious blend African Middle Eastern flavors.
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  • African Diaspora Contributions: Returnees from abroad infuse Kenyan dishes new techniques ingredients reflecting broader continental connections enriching culinary tapestry nation enjoys presently.
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  The Role Of Globalization In Shaping Contemporary Kenyan Dishes:

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  In today’s interconnected world globalization plays pivotal role transforming how people eat what they eat especially true Kenya where exposure international cuisines drives demand innovation locally produced foods catering evolving consumer preferences.

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  • Veganism Trend Surge:
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  1. Rise plant-based diets health consciousness leading popularity vegan-friendly versions traditional meals e.g., jackfruit nyama choma.
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  2. Growing availability vegan products supermarkets restaurants encouraging experimentation alternative ingredients sustainable eating practices.
  • Coffee Culture Revolution:<\/Li> t
    1. Kenya renowned coffee production quality beans globally recognized contributing robust coffee scene domestically growing number cafes roasteries showcasing artisanal brews attracting aficionados worldwide.<\/LI>
    2. Coffee tourism emerging trend attracting visitors eager learn about cultivation processes behind beloved beverage further boosting economy through agritourism initiatives.<\/LI>
  • Fusion Fast Food Phenomenon:<\/LI> t
    1. New fast-food chains integrating global flavors creating hybrid menus appealing younger demographics looking quick tasty options reflect modern lifestyles urban settings.<\/LI>
    2. Incorporation healthier options fast-casual dining chains responding consumer demand nutritious alternatives without compromising taste quality traditional favorites remain popular choices.<\/LI>

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  Ethnic Culinary Traditions Preserved Amidst Modern Innovations:

  In midst rapid changes modern influences preserving ethnic culinary traditions remains vital aspect maintaining cultural identity passing down generations preserving rich heritage embodied recipes handed down ancestors ages past continue thrive alongside innovations present-day kitchens inspire creativity adaptation changing times.

  Kikuyu Cuisine Preservation:<\/L1>
  Dishes like mursik (fermented milk) githeri (corn-bean stew) maintain cultural significance rituals gatherings community members ensuring continuity legacy through everyday meals. Cooking classes workshops organized communities promote learning traditional methods safeguarding knowledge younger generations ensuring sustainability cherished practices.
  Luo Culinary Heritage:<\/L1>
  Dishes such sukuma wiki (collard greens), irio (mixture maize beans potatoes) represent Luo identity often prepared communal settings emphasizing unity cooperation central values culture. Educational programs initiatives encourage exploration understanding Luo gastronomy highlighting importance preserving ancestral wisdom amidst evolving societal norms.

  Kenya’s strategic location along ancient trade routes facilitated cross-cultural exchanges profoundly influencing gastronomic landscape fostering rich tapestry flavors blending African Asian European influences seen contemporary cuisine.

  African Influences:
  Bantu Migration Legacy: Kenya experienced influx Bantu-speaking peoples introducing staple crops cooking techniques still prevalent today e.g., nshima (maize porridge), mboga (vegetable stews). Nilotic Traditions: Nilotic tribes contributed unique dietary preferences emphasizing meat consumption fish Nile River regions inspiring dishes like luwombo (steamed meat wrapped banana leaves). Saharan Connections: Arab traders centuries ago introduced spices cooking methods leaving indelible mark coastal cuisines evident dishes pilau rice biryani biriani. < Eurasian Interactions: Colonial presence British Indian influence introduced tea chai curries altering dietary habits integrating new ingredients transforming local palate preferences. Oriental Infusions: Chinese diaspora established vibrant communities contributing stir-fry noodles dumplings enriching urban culinary scenes creating dynamic interplay East Asian African tastes. < < Mediterranean Echoes: Italian Italianate influences manifest pasta sauces desserts adding layers complexity flavor profiles enjoyed nationwide. < American Inspirations: Western fast-food chains proliferation introducing burgers fries shakes altering eating habits particularly among youth fostering hybridized dietary choices. <

  Nutritional Shifts Reflecting Health Consciousness Trends:<\H2>

  Rising health awareness nutritional consciousness driving changes dietary patterns Kenyans increasingly opting balanced meals incorporating fresh fruits vegetables whole grains minimizing processed foods unhealthy fats sugars.

  Fruits And Vegetables Renaissance: Increased demand locally grown produce markets encouraging consumption variety fruits vegetables essential vitamins minerals supporting overall wellbeing. Farm-to-Table Movement: Growing popularity farm-to-table restaurants promoting sustainable agriculture sourcing ingredients directly farmers ensuring freshness quality minimizing carbon footprint. Vegan And Vegetarian Options Expansion: Rising interest plant-based diets leading expansion vegan vegetarian offerings restaurants cafes catering diverse dietary needs reflecting broader global trend environmental sustainability ethical considerations. Fermented Foods Revival: Traditional fermented foods gaining recognition health benefits probiotics aiding digestion immune function sparking renewed interest reviving ancient practices modern contexts. Nutrition Education Initiatives: Government organizations NGOs launching educational campaigns raising awareness importance balanced nutrition empowering individuals make informed food choices lifestyle choices conducive health longevity. Growing Superfood Trend: Incorporation superfoods quinoa amaranth moringa baobab juice menus cafes restaurants capitalizing nutritional value exotic appeal meeting consumer demand nutritious alternatives. Sustainable Seafood Advocacy: Efforts promote sustainable seafood consumption protecting marine biodiversity educating public responsible fishing practices supporting conservation efforts safeguarding oceanic resources future generations. Ll>Growing Interest Functional Foods: Functional foods enriched nutrients supplements gaining traction addressing specific health concerns boosting immunity improving cognitive performance tailoring individual needs lifestyle demands. Oll>

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  [0]: # Copyright Amazon.com Inc. or its affiliates. All Rights Reserved. [1]: # [2]: # Licensed under the Apache License Version 2.0(the "License"). You may not use this file except in compliance with [3]: # License.A copy ofthe License is located at [4]: # [5]: # http://aws.amazon.com/apache2.0/ [6]: # [7]: # or inthe "license" file accompanying this file.This file is distributed on an "AS IS" BASIS, [8]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,either express or implied.VeIude herein. [9]: # [10]: import logging.config [11]: import botocore.exceptions as boto_exceptions [12]: from sagemaker_containers.beta.framework import content_types, [13]: encoders, [14]: errors, [15]: metric_definitions, [16]: metrics, [17]: model_server_starter, [18]: model_server_starter_config, [19]: worker_pool_config, [20]: worker_config, [21]: data_channels, class ModelServer(model_server_starter.ModelServer): def __init__(self, model_server_start_fn, model_server_start_fn_args=None, num_model_servers=0, max_concurrent_queries_per_model=None, **kwargs): self._logger = logging.getLogger(__name__) self._model_server_start_fn = model_server_start_fn self._model_server_start_fn_args = model_server_start_fn_args self._num_model_servers = num_model_servers self._max_concurrent_queries_per_model = max_concurrent_queries_per_model if 'data_channels' not in kwargs: kwargs['data_channels'] = {} if 'default_data_channel' not in kwargs['data_channels']: kwargs['data_channels']['default_data_channel'] = data_channels.default_data_channel if 'default_content_type' not in kwargs['data_channels']['default_data_channel']: kwargs['data_channels']['default_data_channel']['default_content_type'] = content_types.JSON self._worker_pool_config = worker_pool_config.WorkerPoolConfig(**kwargs) self._model_servers_started = False self._model_servers_stopped = False self._worker_pools_started = False self._worker_pools_stopped = False self._server_ids_to_workers_map = {} self._workers_to_server_ids_map = {} try: logging.config.dictConfig(self.logger_config) logging.getLogger('boto').setLevel(logging.WARNING) logging.getLogger('botocore').setLevel(logging.WARNING) logging.getLogger('urllib3').setLevel(logging.WARNING) logging.getLogger('paramiko.transport').setLevel(logging.WARNING) logging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.WARNING) logging.getLogger('sagemaker_containers.beta.framework.worker_pool').setLevel(logging.INFO) logging.getLogger('sagemaker_containers.beta.framework.model_server').setLevel(logging.INFO) def _get_worker_id(self): return str(id(self)) def _get_workers_for_single_model_server(self): workers_for_single_model_server = [] server_id_to_workers_map_local_copy = None server_id_to_workers_map_local_copy = dict(self.server_ids_to_workers_map) workers_to_server_ids_map_local_copy = dict(self.workers_to_server_ids_map) worker_id_counter_for_current_call_only=0 while len(server_id_to_workers_map_local_copy.keys()) > worker_id_counter_for_current_call_only: worker_id_counter_for_current_call_only+=1 current_worker_id=self.get_worker_id() if current_worker_id not in workers_to_server_ids_map_local_copy.keys(): workers_for_single_model_server.append(current_worker_id) server_ids_of_current_worker=server_id_to_workers_map_local_copy[current_worker_id] del server_id_to_workers_map_local_copy[current_worker_id] del workers_to_server_ids_map_local_copy[current_worker_id] if len(server_ids_of_current_worker)>0: for serverIdOfCurrentWorkerIndexInListServerIdsOfCurrentWorkerInListWorkersForSingleModelServerInLoopOverAllWorkersForSingleModelServerinWhileLoopOverAllWorkersForSingleModelServerinGetWorkersForSingleModelServerinModelServersStartinModelServersStartinModelServersStartinStartinStart: del server_id_to_workers_map_local_copy[ server_ids_of_current_worker[ serverIdOfCurrentWorkerIndexInListServerIdsOfCurrentWorkerInListWorkersForSingleModelServerInLoopOverAllWorkersForSingleModelServerinWhileLoopOverAllWorkersForSingleModelServerinGetWorkersForSingleModelServerinModelServersStartinModelServersStartinModelServersStartinStart]] del workers_to_server_ids_map_local_copy[ server_ids_of_current_worker[ serverIdOfCurrentWorkerIndexInListServerIdsOfCurrentWorkerInListWorkersForSingleModelServerInLoopOverAllWorkersForSingleModelServerinWhileLoopOverAllWorkersForSingleModelServerinGetWorkersForSingleModelServerinModelServersStartinModelServersStartinModelServersStartinStart]] if len( server_id_to_workers_map_local_copy[ server_ids_of_current_worker[ serverIdOfCurrentWorkerIndexInListServerIdsOfCurrentWorkerInListWorkersForSingleModelServerInLoopOverAllWorkersForSingleModelServerinWhileLoopOverAllWorkersForSingleModelServerinGetWorkersForSingleModelServerinModelServersStartinModelServersStartinModelServersStartinStart]]) == ( len( workers_to_server_ids_map_local_copy[ server_ids_of_current_worker[ serverIdOfCurrentWorkerIndexInListServerIdsOfCurrentWorkerInListWorkersForSingleModelServerInLoopOverAllWorkersForSingleModelServerinWhileLoopOverAllWorkersForSingleModelServerinGetWorkersForSingleModelServerinModelServersStartinModelServersStartinModelServersStartinStart]])) == ( len(current_worker_id)): break def _start_multiple_modelservers(self): try: servers=[] num_active_modelservers=self.num_model_servers max_concurrent_queries_per_active_modelservers=self.max_concurrent_queries_per_model active_modelservers_started=False active_modelservers_errored=False modelservers_error_message="" except Exception as ex: error_message=str(ex)+" "+traceback.format_exc() raise errors.InternalError("UnableToParseNumberOfActiveModelserversOrMaxConcurrentQueriesPerModelserversFromEnvironmentVariables:"+error_message) try: try: active_modelservers_started=True active_modelserver=self.modelserverstartfn(*self.modelserverstartfnargs) servers.append(active_modelserver) if max_concurrent_queries_per_active_modelservers != None: active_modelserver.set_max_concurrency(max_concurrent_queries_per_active_modelservers) modelservers_error_message=modelservers_error_message+"UnableToSetMaxConcurrencyOnModelservers:"+str(active_modelserver)+": "+str(max_concurrent_queries_per_active_modelservers)+": "+str(ex)+" "+traceback.format_exc()+" " else: modelservers_error_message=modelservers_error_message+"UnableToSetMaxConcurrencyOnModelserversBecauseMaxConcurrentQueriesPerModelserversWasNotSpecified:"+str(active_modelserver)+": "+str(ex)+" "+traceback.format_exc()+" " active_modelserver.start() modelservers_error_message=modelservers_error_message+"UnableToStarActiveModelservers:"+str(active_modelserver)+": "+str(ex)+" "+traceback.format_exc()+" " except Exception as ex: modelservers_error_message=modelservers_error_message+"UnableToStarActiveModelservers:"+str(active_modelserver)+": "+str(ex)+" "+traceback.format_exc()+" " raise errors.InternalError(modelservers_error_message) num_active_modelserves=num_active_modelserves-1 except Exception as ex: modelserves_error_message=modelserves_error_message+"UnableToStarMultipleModelservers:"+str(s) + ": " + str(num_active_modelserves) + ": " + str(ex) + " " + traceback.format_exc() + " " try: if num_active_modelserves >0 : return self.start_multiple_modelservers() else: return servers else: raise errors.InternalError(modelserves_error_message) else: raise errors.InternalError(modelserves_error_message) else: raise errors.InternalError(modelserves_error_message) else: raise errors.InternalError(modelserves_error_message) else: raise errors.InternalError("TheNumberofActiveModelservesspecifiedbytheEnvironmentVariableSAGEMAKER_NUM_MODEL_SERVERSISNOTAPOSITIVEINTEGERNUMBER"+modelserves_error_messag) else: raise errors.InternalError("TheMaximumNumberofConcurentQueriesPerActiveModelservesspecifiedbytheEnvironmentVariableSAGEMAKER_MAX_CONCURRENT_QUERIES_PER_MODELISNOTANINTEGERNUMBER"+modelserves_error_messag) else: if not active_modelesrvrs_started: raise errors.InternalError("UnableToStarMultipleModelesrvrs"+modelserves_eror_messag) else: raise errors.InvalidArgumentValueException("TheNumberOfActiveModelesrvrsMustBeAPositiveIntegerNumber") else: raise errors.InvalidArgumentValueException("TheMaximumNumberofConcurentQueriesPerActiveModelesrvrsMustBeAnIntegerNumber") else: return servers ***** Tag Data ***** ID: 5 description: Complex method `_start_multiple_model_servers` which handles starting, setting concurrency limits on multiple model servers asynchronously using recursion, start line: 85 end line: 221 dependencies: - type: Method name: _start_multiple_model_servers_recursive_helper_function_wrapper_around_the_internal_function_that_recursively_starts_all_the_specified_number_of_multimodelservers_at_once_and_returns_a_list_with_them_all_when_it_is_done_with_it_all_and_finally_closes_up_the_wrapper_function_by_returning_the_list_from_the_internal_function_after_it_is_done_with_it_all_as_well_ - type: Method/Function/Class Name? start line: N/A - Functionality inferred based on code behavior inside snippet only. algorithmic depth: 5 algorithmic depth external: N obscurity: -5 - Very obscure logic involving recursion within nested exception handling blocks specific only within given code context without clear modular separation elsewhere likely requiring deep understanding of internal state management within recursive calls itself). advanced coding concepts: -5 - Advanced usage of recursion combined with exception handling which can be difficult even for experienced developers given it involves deeply nested blocks specifically tailored within code context itself rather than general recursive patterns alone). interesting for students?: -5 - While complex it might be interesting mainly because it involves unconventional recursive control flow mixed heavily intertwined exception handling mechanism which can serve as advanced case study but may lack practical utility outside very specific scenarios requiring such nuanced control flow management approach). self contained : N/A - Highly contextual depending upon overall class structure functionality purpose thus cannot be isolated easily without considerable modification or recontextualization. ************* ## Suggestions for complexity Here are five advanced ways that could expand or modify logic specific to this code: 1. **Dynamic Configuration Updates**: Implement functionality that allows dynamic updates to `max_concurrent_queries_per_active_modeserve` based on real-time metrics collected during execution. 2. **Fault Tolerance Mechanism**: Add fault tolerance mechanisms that allow failed `active_modeleserve` instances to be retried automatically up to a certain number of attempts before throwing an error. 3. **Parallel Execution Management**: Modify `_start_multiple_modeleserve` method so that it starts multiple `active_modeleserve` instances concurrently using Python's `asyncio` or threading/multiprocessing libraries. 4. **Detailed Logging Enhancements**: Integrate detailed log tracking that includes timestamps, execution duration per `active_modeleserve`, memory usage statistics before/after starting each instance. 5. **Resource Cleanup Hook**: Implement hooks that ensure proper cleanup actions are taken when exceptions occur during `_start_multiple_modeleserve`. This would include releasing any allocated resources or connections properly. ## Conversation <|user|>`I need help understanding why there are so many nested try-except blocks inside [SNIPPET]. It looks overly complicated.`<|end|> <|assistant|>`The nested try-except blocks are designed to handle various failure points separately within `_start_multiple_modeleserve`. Each block attempts different parts of starting multiple modeleserve instances—initializing them (`active_modeleserve=self.modelserverstartfn(*self.modelserverstartfnargs)`), setting concurrency limits (`active_modeleserve.set_max_concurrency(max_concurrent_queries_per_active_modeserve)`), starting them (`active_modeleserve.start()`). If any step fails independently due to different types of exceptions (`ValueError`, generic `Exception`), it logs specific error messages before propagating them upwards.`<|end|> <|user|>`Can we refactor this code so that it uses asynchronous programming instead? I want each modeleserve instance start operation run concurrently.`<|end|> <|assistant|>`Certainly! We can use Python's `asyncio` library here for concurrent operations. Here’s how you can refactor part of your code using asyncio: python import asyncio async def start_single_modeserve(modeleserve_instance): try: await modeleserve_instance.start() return modeleserve_instance.name() except ValueError as ve_ex: error_msg += f"UnableToSetMaxConcurrencyOnModeleServe:{modeleserve_instance}: {max_concurrent_queries_per_active_modeserve}: {ve_ex}n" return None except Exception as ex: error_msg += f"UnableToSetMaxConcurrencyOnModeleServe:{modeleserve_instance}: {ex}n" return None async def _start_multiple_modele_serves_async(): servers=[] tasks=[] while num_active_modele_serves >0 : active_modele_serve=self.modelserverstartfn(*self.modelserverstartfnargs) task=asyncio.create_task(start_single_modeserve(active_modele_serve)) tasks.append(task) num_active_modele_serves -=1 results=await asyncio.gather(*tasks) servers=[res for res in results if res] # Then call your async function using asyncio.run() if __name__ == "__main__": asyncio.run(_start_multiple_modele_serves_async()) This refactored version uses async functions (`async def`) along with `await`, creating tasks concurrently via `asyncio.create_task()`, then gathering results asynchronously using `await asyncio.gather(*tasks)`.`<|end|> <|user|>`What about implementing detailed log tracking? I want logs including timestamps execution duration per modeleserve instance memory usage stats too.`<|end|> <|assistant|>`Sure! You can enhance logging by integrating Python's built-in `logging` module along with additional libraries like `psutil` for memory usage stats. Here’s how you could do it: python import asyncio import psutil import time import logging # Setup logger configuration logging.basicConfig(level=logging.DEBUG) logger=logging.getLogger(__name__) async def start_single_modeserve(modeleserve_instance): start_time=time.time() process=psutil.Process() mem_before=start_time_memory_usage=process.memory_info().rss /1024 /1024 try: await modeleserve_instance.start() exec_duration=time.time()-start_time mem_after=end_memory_usage=process.memory_info().rss /1024 /1024 logger.debug(f"{modeleserve_instance.name()} started successfully | Duration={exec_duration}s | MemoryUsage={mem_after-mem_before}MB") return modeleserve_instance.name() except ValueError as ve_ex: exec_duration=time.time()-start_time mem_after=end_memory_usage=process.memory_info().rss /1024 /1024 logger.error(f"{modeleserve_instance.name()} failed | Error={ve_ex} | Duration={exec_duration}s | MemoryUsage={mem_after-mem_before}MB") return None except Exception as ex: exec_duration=time.time()-start_time mem_after=end_memory_usage=process.memory_info().rss /1024 /1024 logger.error(f"{modeleserve_instance.name()} failed | Error={ex} | Duration={exec_duration}s | MemoryUsage={mem_after-mem_before}MB") return None async def _start_multiple_modes serves_async(): servers=[] tasks=[] while num_active_modes serves >0 : active modes serve=self.modelservestartfn (*self.modelservestartfnargs ) task=asyncio.create_task(start_single_modes serve(active modes serve)) tasks.append(task ) num_act ive modes serves -=1 results await asyncio.gather (*tasks ) servers=[resfor res ins results if res ] # Then call your async function using asyncio.run() if __name__ == "__main__": asyncio.run(_starts multiple modes serves_async()) This setup uses Python's `psutil` library to track memory usage before (`mem_before`)and after (`mem_after`) starting each mode serve instance alongwith execution duration (`exec_duration`). Logs contain detailed information including timestamps automatically handled by default configuration level DEBUG.`<|end|) *** Excerpt *** These observations suggest two hypotheses regarding ZIKV infection during pregnancy.Firstly,, which would leadSecondly,, which would resultWe tested these hypotheses experimentally using our previously described mouse model system [6]. Briefly,. We confirmed our previous findings showing that ZIKV preferentially infects neural progenitor cells rather than mature neurons [6], although we note here that both cell types were infected (). Moreover,, indicating early tropism toward neural progenitor cells (). Thus,, consistent with previous studies showing infection across three trimesters [12]. However,, suggesting limited replication at these later time points (). Therefore,, whereas placental infection was observed at all time points examined (). We also confirmed previous findings demonstrating severe fetal abnormalities following mid-gestation maternal infection (), whereas no obvious abnormalities were detected following late gestational infections (). *** Revision *** ## Plan To create an exercise that challenges advanced comprehension skills along with requiring profound factual knowledge beyond what is presented in the excerpt requires several modifications: - Introduce more scientific terminology relevant but not explicitly defined within the text itself; readers must infer meaning from context or possess prior knowledge. - Include references to specific genes or proteins involved in ZIKV pathogenesis without explicit explanation—forcing readers either already familiar with virology/genetics concepts or those who will research further. - Embed complex logical structures involving counterfactual reasoning ("If X had not occurred...") alongside conditionals ("If X occurs..."). - Increase complexity through longer sentences containing multiple clauses that require careful parsing to understand relationships between ideas. By incorporating these elements into rewritten excerpts, we increase both linguistic complexity—demanding higher-level language processing skills—and factual density—requ