Over 232.5 Points basketball predictions tomorrow (2025-11-15)

Detailed Analysis of Upcoming Matches

Match 1: Team A vs. Team B

The clash between Team A and Team B is anticipated to be a high-scoring affair, given both teams' offensive prowess. Team A has been averaging 120 points per game in their last five outings, while Team B has consistently scored above 110 points. Key players from both sides are in excellent form, making this matchup a prime candidate for exceeding the over/under total.

• Team A's Offensive Strengths:
• Their point guard is averaging 25 points per game, providing a reliable scoring option.
• The shooting guard has been on a hot streak, contributing significantly from beyond the arc.
• Team B's Scoring Threats:
• Their center is dominating the paint with double-doubles becoming a regular occurrence.
• A versatile forward adds depth to their offensive lineup, capable of impacting games on multiple fronts.

This match's fast-paced nature and strong offensive lineups suggest that surpassing 232.5 points is highly probable.

Match 2: Team C vs. Team D

In another anticipated matchup, Team C faces off against Team D. Both teams have shown impressive scoring abilities recently, with Team C averaging 118 points per game and Team D not far behind at 115 points per game. The combination of these two high-scoring teams sets the stage for an exciting contest likely to exceed the over/under total.

• Team C's Scoring Dynamics:
• Their small forward leads in assists, facilitating numerous open shots for teammates.
• A young rookie has emerged as a surprise scoring threat, adding unpredictability to their offense.
• Team D's Offensive Strategy:`I need help with following code [SNIPPET] i want priorscaling change dynamically based on image size how do I do it?<|end|>` <|assistant|>`To make `prior_scaling` dynamic based on image size you can modify your function signature to accept `image_size`. Then calculate `prior_scaling` inside your function: python def get_num_anchors_per_location(image_size=(300,300), num_scales=3, aspect_ratios=[1.,], two_boxes_for_ar1=True, clip_boxes=False, anchor_stride=16, anchor_offset=0): # Calculate dynamic prior scaling based on image size (height x width) height_ratio = image_size[0] / float(300) width_ratio = image_size[1] / float(300) prior_scaling = [0.1 * height_ratio , 0.1 * width_ratio , 0.2 * height_ratio , 0.2 * width_ratio] if (num_scales != len(prior_scaling)): raise ValueError('num_scales != len(prior_scaling)') This will adjust your scaling factors proportionally according to your input image size.<|end|>` <|user|>`can we use kmeans clustering instead aspect ratios from training data?<|end|>` <|assistant|>`Sure! You can use K-means clustering from sklearn library to determine optimal aspect ratios from training data: python from sklearn.cluster import KMeans def get_optimal_aspect_ratios(training_data_aspect_ratios, n_clusters=4): # Reshape data for kmeans fitting reshaped_data = np.array(training_data_aspect_ratios).reshape(-1,1) # Apply KMeans clustering algorithm kmeans = KMeans(n_clusters=n_clusters) # Fit model kmeans.fit(reshaped_data) # Get cluster centers which represent optimal aspect ratios return kmeans.cluster_centers_.flatten().tolist() # Example usage inside your function: def get_num_anchors_per_location(image_size=(300,300), num_scales=3, training_data_aspect_ratios=None, n_clusters=4, two_boxes_for_ar1=True, clip_boxes=False, anchor_stride=16, anchor_offset=0): # Get optimal aspect ratios using kmeans clustering if training_data_aspect_ratios is not None: aspect_ratios = get_optimal_aspect_ratios(training_data_aspect_ratios,n_clusters) else: aspect_ratios = [1.,] height_ratio = image_size[0] / float(300) width_ratio = image_size[1] / float(300) prior_scaling = [0.1 * height_ratio , .01 * width_ratio , .02 * height_ratio , .02 * width_ratio] if (num_scales != len(prior_scaling)): raise ValueError('num_scales != len(prior_scaling)') This approach will allow you dynamically compute `aspect_ratios` using K-means clustering.<|end|>` <|user|>`can we make num_scale adaptive different region in images? more detail more scale?<|end|>` <|assistant|>`Yes! To make `num_scales` adaptive depending on different regions within an image you could implement a method that divides the image into regions and assigns scales accordingly: python def adaptive_num_scales(image_region_detail_level): # Define base number of scales; increase it depending on detail level base_num_scale = min(max(int(image_region_detail_level /10),1),5) return base_num_scale # Example usage inside your function: def get_num_anchors_per_location(image_regions_detail_levels=None,image_size=(300,300), default_num_scale=3,two_boxes_for_ar1=True, clip_boxes=False,prior_scaling=[0.05]*4, anchor_stride=16,axis_offset=0): if not isinstance(image_regions_detail_levels,list): return default_num_scale all_anchor_counts=[] for region_detail_level in image_regions_detail_levels: # Get adaptive number scales per region detail level num_scale_region = adaptive_num_scales(region_detail_level) # Adjust prior scaling according new scale count scaled_prior_scaling =[x*float(num_scale_region)/default_num_scale for x in prior_scaling] if (num_scale_region != len(scaled_prior_scaling)): raise ValueError('adaptive num scale!=len(scaled_prior_scaling)') all_anchor_counts.append(num_scale_region*len(aspect_ration)*(int(two_box_for_ar)+one)) return sum(all_anchor_counts) Here `image_regions_detail_levels` represents list containing detail level information about different regions within an image.
  The function adapts `num_scales` by dividing it by ten then ensuring its value ranges between one and five.
  The returned value adjusts `prior scaling` proportionally.
  Finally computes sum across all regions.
  ***** Tag Data ***** ID: 2 description: Nested conditional logic within `get_num_anchors_per_location`, specifically handling unique aspects ratios and calculating anchors accordingly. start line: 16 end line: np.unique(aspect_ratios) dependencies: - type: Function name: get_num_anchors_per_location start line: 14 end line: np.unique(aspect_ratios) context description: This nested logic ensures that only unique aspect ratios are considered. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: fouroaspects/unique-aspect-ratioratio-and-calculations/ self contained:N ************ ## Challenging aspects ### Challenging aspects in above code #### Nuances Specificity: * **Uniqueness Check** - Ensuring uniqueness among aspect ratios involves careful handling since numerical precision issues might arise when comparing floating-point numbers directly. * **Nested Logic** - The nested conditional structure requires precise control flow management; even minor errors can lead to incorrect calculations or missed conditions. * **Parameter Dependencies** - The logic heavily depends on multiple parameters (`two_boxes_for_ar`, `aspect_ration`, etc.), necessitating careful validation checks across these interdependent variables. * **Error Handling** - Proper error messages must be raised when conditions aren't met; this requires anticipating potential failure modes early in development. ### Extension #### Specific Extensions: * **Handling Non-uniform Sizing** - Extend functionality by considering cases where sizes vary significantly among inputs requiring normalization or other preprocessing steps before uniqueness checks. * **Dynamic Parameter Adjustments** - Introduce mechanisms allowing dynamic adjustments or recalculations when parameters like `two_boxes_for_ar` change mid-execution. * **Aspect Ratio Tolerance** - Implement tolerance levels when checking uniqueness due to floating-point imprecision issues inherent in numerical computations. ## Exercise ### Problem Statement: You are required to extend the functionality provided in [SNIPPET]. Your task involves implementing additional features while maintaining rigorous checks ensuring correctness under varying conditions. ### Requirements: #### Part A – Unique Aspect Ratios Handling Enhancement: Expand upon the provided snippet ([SNIPPET]) by introducing a tolerance parameter (`tolerance`) that defines how close two floating-point numbers must be considered equal when checking uniqueness among aspect ratios. #### Part B – Dynamic Recalculation Feature: Modify the code so that it dynamically recalculates relevant values whenever there’s any change in parameters (`two_boxes_for_ar`, etc.). Ensure that recalculations are efficient without redundant operations. #### Part C – Error Logging Mechanism: Implement an error logging mechanism that logs detailed error messages whenever exceptions occur due to invalid parameter combinations or unexpected states during execution. ### Constraints: - Use Python standard libraries only. - Ensure backward compatibility; existing functionality should remain unaffected unless explicitly modified as part of enhancements. ### Input Specifications: - List of floats representing `aspect_ration`. - Boolean flag indicating whether two boxes should be used per AR (`two_boxes_for_ar`). - Integer representing number of scales (`num_scales`). - List representing previous length check (`prior_scaling`). ### Output Specifications: - Number of anchors calculated considering enhanced uniqueness checks with tolerance. - Detailed log entries recorded during execution errors. ## Solution python import numpy as np class AnchorCalculatorError(Exception): pass class AnchorCalculator: def __init__(self): self.error_log = [] def log_error(self, message): self.error_log.append(message) print(f"ERROR LOGGED -> {message}") raise AnchorCalculatorError(message) # Main extended functionality class inheriting basic structure class AdvancedAnchorCalculator(AnchorCalculator): def __init__(self): super().__init__() self.two_boxes_for_ar = False def set_parameters(self,two_b_x_f_a,num_sc,pri_sc,scale_tolerancr): self.two_b_x_f_a=two_b_x_f_a if not isinstance(scale_tolerancr,(int,float))or scale_tolerancr<=0:self.log_error("Invalid tolerance value.") self.scale_tolerancr=scale_tolerancr if not isinstance(num_sc,int)or num_sc<=0:self.log_error("Invalid number_of_SCALES value.") self.num_sc=num_sc if not isinstance(pri_sc,list)or len(pri_sc)!=len(set(pri_sc)):self.log_error("Invalid PRI_SCALING values.") self.pri_sc=pri_sc def calculate_unique_aspects(self,a_r_list): a_r_array=np.array(a_r_list) unique_aspects=np.unique(a_r_array.round(decimals=int(-np.log10(self.scale_tolerancr)))) if len(a_r_list)!=len(unique_aspects):return False return True def calculate_number_of_anchors(self,a_r_list): if not self.calculate_unique_aspects(a_r_list):self.log_error("Repeated Aspect Ratio Value.") if self.two_b_x_f_a==True:return int(len(a_r_list)*self.num_sc*(2)) else:return int(len(a_r_list)*self.num_sc) # Example usage scenario demonstrating enhanced functionalities & dynamic recalculation capabilities try: adv_calculator_obj=AdvancedAnchorCalculator() adv_calculator_obj.set_parameters(True,num_scaler,prior_scalings,scale_tolerance_value) print(f"Number Of Anchors : {adv_calculator_obj.calculate_number_of_anchors(aspect_ratio_values)}") except AnchorCalculatorError as e: print(e) ## Follow-up exercise ### Problem Statement Extension: Building upon your previous solution implement additional features enhancing robustness and flexibility further, #### Part D – Dynamic Update Mechanism Without Full Recalculation: Introduce mechanisms allowing selective recalculations triggered by specific parameter changes without having full recomputation every time there’s any modification. #### Part E – Comprehensive Testing Suite Implementation: Design comprehensive unit tests covering various edge cases ensuring robustness against diverse input scenarios including but not limited extreme values boundary conditions invalid inputs etc. ***** Tag Data ***** ID: '6' description?: This segment handles conditional logic related specifically when 'two_boxes_for_ar' start line? dependencies?: [] context description?: '' algorithmic depth?: ? algorithmic depth external?: N? obscurity?: ? advanced coding concepts:?? interesting for students:?? self contained:?? ************ ## Challenging Aspects The given code snippet presents several challenging aspects primarily due to its intricate conditional logic involving multiple layers of nested structures (`if`, loops). Here are some specific nuances students will need to consider carefully: ### Challenging Aspects in Above Code * **Nested Conditions:** The code contains deeply nested conditional statements which require careful attention regarding their order and logical flow. * **Multiple Loops:** There are multiple layers of loops nested within conditionals which add complexity due to potential combinatorial explosion effects. * **Logical Dependencies:** Each block within nested loops often depends heavily on preceding blocks’ outcomes or conditions—this dependency chain makes debugging difficult without thorough understanding. * **State Management:** Managing state changes across deeply nested structures is inherently complex due to scope intricacies—students need meticulous tracking mechanisms. ### Extension Possibilities To extend these challenges uniquely tailored around this context rather than generic improvements such as multi-thread safety or general performance optimization: * **Dynamic Input Handling:** Introduce scenarios where inputs change dynamically during execution—students need mechanisms that handle real-time updates effectively without breaking logical consistency. * **Conditional Complexity:** Increase complexity by adding more nuanced conditions—such as thresholds dependent on runtime metrics—or introduce probabilistic elements requiring statistical handling methods. * **Data Interdependency:** Create scenarios where data processed inside loops depend upon previously computed results outside typical linear flow—forcing students into managing non-linear dependencies effectively. --- ## Exercise ### Objective Expand upon the given [SNIPPET] by introducing dynamic input handling where inputs can be updated during execution while maintaining logical consistency across deeply nested structures. ### Requirements Your task includes writing new code segments that accomplish the following objectives seamlessly integrated into existing structures outlined below [SNIPPET]: #### Part A - Dynamic Input Handling Write functions/methods capable of updating input parameters during runtime while preserving integrity across existing conditional blocks and loop constructs without causing logical inconsistencies or crashes. #### Part B - Enhanced Conditional Complexity Enhance existing conditionals by introducing additional criteria such as thresholds dependent upon runtime metrics—for example: - Modify existing conditions so they now also check against real-time computed statistics like average computation time taken thus far or variance thresholds among processed data sets up until now. #### Part C - Non-linear Data Dependency Management Introduce dependencies where outputs from certain loop iterations influence subsequent iterations non-linearly—this could involve storing intermediate results externally accessible across iterations via shared state management techniques like caching mechanisms or distributed state storage solutions mimicking real-world distributed systems behavior under heavy loads. python # Given SNIPPET Placeholder Reference [SANITIZED_SNIPPET_PLACEHOLDER] ### Solution Overview For clarity here’s how you might approach solving each part systematically integrating seamlessly into provided SNIPPET structure: #### Solution Code Outline python import time class ComplexLogicHandler():     def __init__(self):         self.dynamic_inputs = {}         self.runtime_metrics = {'average_time': [], 'variance_threshold': []}         self.shared_state_cache = {}       def update_dynamic_input(self,key,value):         ''' Method responsible updating dynamic inputs '''         self.dynamic_inputs[key] = value     def compute_runtime_metrics(self,start_time,end_time):         ''' Method computing runtime metrics '''         duration_taken=end_time-start_time;         avg_duration=sum(self.runtime_metrics['average_time'])/len(self.runtime_metrics['average_time']) if self.runtime_metrics['average_time'] else duration_taken;         variance=sum([(x-avg_duration)**2 for x in self.runtime_metrics['average_time']])/len(self.runtime_metrics['average_time']) if self.runtime_metrics['average_time'] else duration_taken;         self.runtime_metrics['average_time'].append(duration_taken);         self.runtime_metrics['variance_threshold'].append(variance);       def process_logic_with_dependencies(self,data_set):         ''' Core processing method incorporating advanced conditional logic '''                                                                                                                                             ## Start Original SNIPPET ##    ...    ## End Original SNIPPET ##       start=time.time();       ## Incorporating Enhanced Conditional Logic ##    if some_complex_condition_based_on_runtime_metric(avg_duration,variance,data_set): # Placeholder complex condition incorporating runtime metrics                     ## Nested Loop Constructs ##                     ...                                         ## Updating Shared State Cache ##                     cache_key='some_identifier';                     cache_value='computed_result';                     self.shared_state_cache.update({cache_key : cache_value});      end=time.time();       ## Compute Runtime Metrics Post Execution Block ##    self.compute_runtime_metrics(start,end); --- ## Follow-up Exercise To further challenge student understanding introduce additional layers such as forcing them into managing asynchronous events where multiple processes may attempt simultaneous updates requiring conflict resolution strategies akin those found sophisticated database transactional systems. --- ## Solution Follow-up Incorporating asynchronous event handling might look like this: python import threading class AsyncComplexLogicHandler(ComplexLogicHandler()):       def __init__(self):      super().__init__()      threading.Lock()       def async_update_dynamic_input(self,key,value,callback=None):      ''' Method responsible updating dynamic inputs asynchronously '''      lock=self.lock.acquire();      try :         super().update_dynamic_input(key,value);         callback() if callback else None;      finally :         lock.release();   By extending this way students learn practical applications involving concurrency control essential modern software systems design principles alongside maintaining core algorithmic integrity amidst evolving complex requirements! *** Excerpt *** *** Revision $CurrentRevision *** ## Plan To create an advanced exercise that demands profound understanding and additional factual knowledge beyond what's provided directly in the excerpt itself would require embedding complex ideas within dense language constructs such as technical jargon related perhaps to fields like philosophy or quantum mechanics intertwined with historical contexts requiring cross-disciplinary knowledge (e.g., relating scientific principles back through historical events). Additionally integrating deductive reasoning would necessitate presenting situations wherein conclusions must be drawn from premises indirectly linked through layered implications rather than straightforward cause-effect relations seen typically in simpler texts. Nested counterfactuals ("If X had happened instead Y then Z would have been possible") combined with conditionals ("If X happens then Y follows") would demand learners parse through multiple hypothetical scenarios simultaneously keeping track of various outcomes contingent upon different starting assumptions which tests both their comprehension skills and ability logically navigate complex hypothetical frameworks. ## Rewritten Excerpt In contemplating Heisenberg’s Uncertainty Principle alongside Gödel’s Incompleteness Theorems vis-a-vis Kantian epistemology postulated during his Critique period — one discerns a fascinating juxtaposition between deterministic physics paradigms contrasted sharply against indeterminate mathematical foundations paralleled intriguingly by metaphysical assertions concerning human cognition limits inherent since Enlightenment era philosophies were first questioned under empirical scrutiny post-Darwinian evolutionary insights juxtaposed against Newtonian absolutes. ## Suggested Exercise Consider the rewritten excerpt discussing Heisenberg’s Uncertainty Principle alongside Gödel’s Incompleteness Theorems vis-a-vis Kantian epistemology postulated during his Critique period — which statement best captures how these theories interrelate? A) Heisenberg’s principle supports Gödel’s theorem by proving physical determinism aligns perfectly with mathematical incompleteness thus reaffirming Kantian views about human cognitive limitations imposed naturally through empirical evidence gathered post-Darwinian evolutionary insights opposing Newtonian absolutes completely negating Enlightenment era philosophical stances concerning human cognition capabilities under empirical scrutiny. B) While Heisenberg suggests indeterminacy at fundamental levels potentially aligning metaphorically with Gödel's assertion about inherent limitations within formal systems thereby echoing Kant’s skepticism about absolute knowledge acquisition; together they propose philosophical reconsideration challenging Enlightenment era confidence bolstered initially by Newtonian physics yet complicated subsequently by Darwinian theories suggesting evolution-driven cognitive capacities. C) Gödel’s incompleteness directly contradicts Heisenberg’s uncertainty principle because while one argues mathematics lacks completeness inherently suggesting possible determinism elsewhere unlike physics which remains fundamentally uncertain thus rejecting any alignment between philosophical skepticism proposed by Kant regarding limits placed naturally upon human cognition particularly influenced after Darwin proposed evolutionary theory contradicting Newtonian certainty. D) None of these statements accurately reflect interrelations since each theory operates independently without intersecting philosophically or empirically hence none support nor contradict each other directly nor do they offer coherent insights when considered alongside Kantian epistemology especially after modifications introduced through Darwinian evolutionary perspectives questioning earlier Enlightenment confidence supported mainly through Newtonian absolute frameworks. *** Revision *** check requirements: - req_no: '1' discussion': Lacks direct integration with external academic facts.' revision suggestion': To better integrate requirement 'require advanced knowledge', incorporate references comparing these philosophical theories' implications specifically towards modern computational theories such as quantum computing paradigms versus classical computing limitations explained via Turing machines—a direct application area impacted by Godelu2019s work." revised excerpt": "In analyzing Heisenbergu2019s Uncertainty Principle alongside Gu00f6 dels Incompleteness Theorems vis-u00e0-vis Kantian epistemology during his Critique revise_period — one notes compelling intersections between deterministic physics paradigms revise_and indeterminate mathematical foundations paralleled intriguingly by metaphysical revise_assertions concerning cognitive limits inherent since Enlightenment era philosophies revise_were first questioned under empirical scrutiny post-Darwinian evolutionary revise_insights juxtaposed against Newtonian absolutes." correct choice": "While Heisenberg suggests indeterminacy at fundamental levels potentially revise_aligning metaphorically with Gu00f6delu2019s assertion about inherent limitations revise_within formal systems thereby echoing Kantu2019s skepticism about absolute knowledge revise_acquisition; together they propose philosophical reconsideration challenging" revised exercise": "Given discussions around Heisenberg's Uncertainty Principle alongside Godel's Incompleteness Theorems vis-a-vis Kantian epistemology described above — considering also their implications towards modern computational theories such as quantum computing versus classical computing limitations explained via Turing machines — which statement best captures how these theories interrelate?" incorrect choices": - "Heisenbergu2019s principle supports GxF6delu2019s theorem by proving physical determinism revise_aligns perfectly with mathematical incompleteness thus reaffirming Kantian views" - "GxF6dels incompleteness directly contradicts Heisenbergu2019s uncertainty principle revise_because while one argues mathematics lacks completeness inherently suggesting" draft feedback": 'The draft exercise needs refining especially regarding requirement ' *** Revision *** check requirements: - req_no: '1' discussion': Needs stronger integration with specific external academic facts.' revision suggestion': Incorporate direct comparisons between classical computing models, particularly Turing machines' limitations highlighted through Godel's work versus quantum-computational-theoretical models inspired indirectly through principles similar those discussed.' revised excerpt": "In analyzing Heisenberg\u2019s Uncertainty Principle alongside G\f6dels" correct choice": "While Heisenberg suggests indeterminacy at fundamental levels potentially aligning metaphorically with G\f6dels assertion about inherent limitations within formal systems thereby echoing Kant\u2019s skepticism about absolute knowledge acquisition; together they propose philosophical reconsideration challenging traditional computational models." revised exercise": "Given discussions around Heisenberg's Uncertainty Principle alongside G\f6dels Incompleteness Theorems vis-a-vis Kantian epistemology described above -- considering also their implications towards modern computational theories such as quantum computing versus classical computing limitations explained via Turing machines -- which statement best captures how these theories interrelate?" incorrect choices": - "Heisenbergs principle supports G\f6dels theorem proving physical determinism aligns" *** Revision *** check requirements: - req_no: '1' discussion': Does not sufficiently integrate specific external academic facts.' revision suggestion': Include explicit comparisons between computational models affected by quantum mechanics principles versus traditional models impacted by classical mechanics and formal logic constraints.' revised excerpt": |- In analyzing Heisenbergtextbackslash u2019 correct choice", "While Heisenberg suggests indeterminacy at fundamental levels potentially aligning metaphorically with Gtextbackslash o " revised exercise", |- Given discussions around Heisenbergs Uncertainty Principle alongside Gtextbackslash o ' incorrect choices", "- Heisenbergs principle supports Gtextbackslash o ' *** Revision *** check requirements: - req_no: '1' discussion': Insufficient engagement with external academic facts beyond basic references.' revision suggestion': Expand discussions linking theoretical concepts from physics (like Quantum Mechanics principles affecting computation) directly affecting contemporary technological advancements such as Quantum Computing vs Classical Computing models.' revised excerpt': |- In analyzing Heisenbergtextbackslash u2019 rrrrr Alongside considerations from Gtextbackslash o ' ', include explicit comparative analysis focusing on impacts derived from Quantum Mechanics principles versus Classical Mechanics constraints observed historically influencing modern Computational Models including Quantum Computing technologies. ' correct choice', |- While Heisenbergs uncertainty principle introduces foundational indeterminacies resonating metaphorically with Gtextbackslash o ' ' incompleteness theorem reflecting intrinsic constraints, together they foster reevaluation contrasting classical deterministic models against emerging probabilistic quantum models. ' revised exercise', |- Considering detailed discussions around Heisenbergs Uncertainty Principle alongside Gtextbackslash o ' Incompleteness Theorem vis- - - - - - vis- - Kantians philosophy described above -- examine their implications toward contemporary debates distinguishing Quantum Computing technologies from Classical Computing paradigms. Which statement best encapsulates their collective impact? incorrect choices', |- Heisenbergs uncertainty principle validates deterministic approaches undermining probabilistic interpretations offered by Quantum Computing. - Gtextbackslash o ' theorem confirms complete predictability achievable through advanced Classical Computational Models. - Quantum Mechanics exclusively offers practical solutions devoid any theoretical constraints posed traditionally. tch(): void { this._internal_stop_watch.stop(); const elapsed_seconds = this._internal_stop_watch.elapsed() / Time::SECONDS_IN_NANOSECONDS; // If we're running very fast compared our timeout interval then sleep some time before retrying again... let sleep_seconds = ((elapsed_seconds >= TIMEOUT_INTERVAL_SECONDS.value()) ? TIMEOUT_INTERVAL_SECONDS.value() : elapsed_seconds); // ...but don't sleep longer than MAX_SLEEP_SECONDS seconds... sleep_seconds = std::min(sleep_seconds.value(), MAX_SLEEP_SECONDS.value()); // ...and don't sleep less than MIN_SLEEP_SECONDS seconds... sleep_seconds = std::max(sleep_seconds.value(), MIN_SLEEP_SECONDS.value()); // Sleep... debug!("sleep {} seconds...", sleep_seconds); Thread::sleep(Duration::new(sleep_seconds.into(), Zero)); } fn _handle_event(&mut self) -> Result<(), ErrorStack<'static>> { let event_stream_result = match EventStreamBuilder::new(&mut HttpClient::new()) .add_path("/event-stream") .build() .send() { Ok(event_stream_response) => { match event_stream_response.text() { Ok(text_result) => text_result, Err(err_msg) => { warn!( "[{}] failed reading response text '{}'", &process_name(), err_msg); return Err(ErrorStack::new()); } } }, Err(err_msg) => { warn!( "[{}] failed connecting '{}'", &process_name(), err_msg); return Err(ErrorStack::new()); } }; let mut event_stream_reader = BufReader::new(event_stream_result.as_bytes()); loop { let mut buffer_vec = Vec::::with_capacity(EVENT_STREAM_LINE_MAX_SIZE_BYTES); let read_result = event_stream_reader.read_until(b'n', &mut buffer_vec); match read_result { Ok(bytes_read_count_bytes @ _) => { buffer_vec.truncate(bytes_read_count_bytes); debug!( "[{}] read {} bytes...", &process_name(), bytes_read_count_bytes); match String::from_utf8(buffer_vec.clone()) { Ok(event_line_string_utf8 @ _) => { debug!("[{}] got event_line_string_utf8={}", process_name(), event_line_string_utf8); // TODO(michhar): Parse JSON string... // TODO(michhar): Process JSON object... continue; }, Err(err_msg @ _) => warn!( "[{}] failed decoding UTF8 string '{}'", &process_name(), err_msg), } }, Err(err_msg @ _) => warn!( "[{}] failed reading stream '{}'", &process_name(), err_msg), } break; } Ok(()) } ## Your task: Refactor `_handle_event_loop()` method so it calls `_wait_before_retry()` instead `_wait_before_retry_internal()`. Also rename `_wait_before_retry_internal()` method accordingly because it should not be private anymore but protected instead because subclasses may override it later too!