TAURON Liga Women stats & predictions

KS Developres Rzeszów

KS Developres Rzeszów has been a dominant force in Polish volleyball for years. Their success can be attributed to a well-rounded roster featuring experienced players who excel in both offense and defense. The team's libero has been particularly impressive this season, making crucial digs and maintaining stability on the court.

PGE Skra Bełchatów

PGE Skra Bełchatów is known for their high-scoring games and aggressive attacking style. With a lineup full of talented hitters, they pose a significant threat to any opponent. However, their sometimes inconsistent defense can be exploited by teams with strong serving strategies.

Impel Wrocław

Impel Wrocław has emerged as one of the most exciting teams in recent seasons. Their young roster brings fresh energy and innovative plays that have caught many seasoned teams off guard. Their coach has been praised for fostering a culture of teamwork and resilience among players.

MKS Dąbrowa Górnicza

MKS Dąbrowa Górnicza relies heavily on their experienced libero who provides stability at the backline. While they may not boast as many star players as other top teams, their cohesive unit often pulls off surprising victories through sheer determination and tactical discipline.

Volleyball Strategies: What Sets Top Teams Apart?

In any competitive sport like volleyball, strategy plays an integral role in determining outcomes on match day. Let’s explore some common strategies employed by top-tier teams within the TAURON Liga Women Poland:

• Serving Strategy: Serving is often considered one of the most important aspects of volleyball because it initiates play from each side’s perspective; thus making it crucially important how well teams execute this skill during matches.
  
• Bloc Strategy:A good block can completely neutralize opposing attacks or even score points directly when executed perfectly.
  
• Digging Technique:An effective dig can turn around momentum during close games.
  
• Sets & Spikes:
  A well-placed set allows hitters more time before they must attack; thus providing them with an opportunity to score points.
  
• Foul Play & Faults:
  Fouls committed by either side can lead directly towards point deductions which ultimately affect game outcomes.
  
• Mental Toughness:
  The ability to stay focused under pressure while maintaining composure during intense rallies sets apart great players from good ones.
  
• Cohesive Teamwork:
  A harmonious combination between individual skills contributes towards building successful plays.
  
• Cheerleading & Support:
  I need help with initializing more complex attributes dynamically based on some runtime conditions [SNIPPET]<|end|> <|assistant|>Sure! To dynamically initialize attributes based on runtime conditions within your `__init__` method: python def __init__(self): self.job_id = None self.project_id = None self.component_code = None self.version = None self.bk_biz_id = None self.description = None self.inputs = [] # Dynamic attribute initialization example: config_conditions = get_runtime_config_conditions() if config_conditions.get('add_extra_attributes'): setattr(self.__class__, 'extra_attribute', 'default_value') setattr(self.__class__, 'another_attribute', []) Here `get_runtime_config_conditions()` would be your function fetching runtime configurations. Would you like further customization? 1] * len(footnotes) footnotes.append(f"^{i + offset + len(footnotes)}") i += offset + len(footnotes) - i - offset - len(footnotes) text_parts.append(text[start:i]) text_parts.append(f'^{footnote}') start = i + offset + len(footnotes) footnote_index += offset + len(footnotes) if start != len(text): text_parts.append(text[start:]) return "".join(text_parts), footnotes # Example usage: processor_instance = FootnoteProcessor() text_input_with_footnotes_and_tags_1_0_0_0_0_0_0_0_0( """ This is some text with {^1} footnotes{^2} inline{^}. And here we have another footnote reference {^}. """, processor_instance) ### Follow-up exercise **Follow-up Exercise Description**: Enhance your implementation further by introducing support for nested footnotes references within footnote texts themselves (e.g., `{^nested_note{^inner_note}...}`). Ensure that nested references are resolved correctly without losing track of outer references. **Requirements**: - Extend parsing logic so nested footnote references within existing footnotes are correctly identified. - Maintain consistency across all levels ensuring no index conflicts arise between outer notes referencing inner notes. - Optimize performance ensuring minimal overhead despite increased complexity. ### Solution: python import re class FootnoteProcessor: def __init__(self): pass def text_input_with_footnotes_and_tags_nested_processor( text_input_with_footnotes_and_tags_1_0_0_0_0_0_0, ): def parse_nested_footnote_references(text): pattern_outermost_opening_brace_indices_regexps_combined_pattern_string_complile_obj = re.compile(r"{(.*?)}") nested_pattern_innermost_opening_brace_indices_regexps_combined_pattern_string_complile_obj = re.compile(r"{(.*?)}") all_matches_outermost_level_indices_tuples_list = pattern_outermost_opening_brace_indices_regexps_combined_pattern_string_complile_obj.findall(text) all_matches_innermost_level_indices_tuples_list = [] updated_text_segments_list=[] last_end_index=0 def resolve_inner_references(matched_text_segment): nonlocal last_end_index inner_matches=nested_pattern_innermost_opening_brace_indices_regexps_combined_pattern_string_complile_obj.findall(matched_text_segment) resolved_segment=matched_text_segment if inner_matches: resolved_segment="" prev_end=last_end_index inner_offset=last_end_index current_offset=prev_end inner_reference_counts=len(inner_matches) inner_footnote_counter=[f"^{{i+inner_offset+current_offset}}" for i in range(inner_reference_counts)] current_offset+=len(inner_footnote_counter)-inner_reference_counts inner_start_index=prev_end+len(inner_matches.pop(0)) current_offset+=len(inner_matches.pop()) while inner_matches: current_match=inner_matches.pop() current_match_len=len(current_match) resolved_segment+=resolved_segment[current_start_index:] current_start_index=current_match_len resolved_segment+=f"^{{i+inner_offset+current_offset}}" current_offset+=len(inner_footnote_counter)-inner_reference_counts prev_end=current_match_len inner_start_index=prev_end+len(current_match) current_offset+=len(current_match) resolved_segment+=resolved_segment[current_start_index:] else: resolved_segment=f"{matched_text_segment}^{{last_end_index}}" return resolved_segment # Example usage: processor_instance_nested_example=FootnoteProcessor() result_text,result_foot_notes=text_input_with_footnotes_and_tags_nested_processor( """ This is some text with {^1} footnotes {^nested_note{^inner_note}} inline{^}. And here we have another footnote reference {^}. """, processor_instance_nested_example) print(result_text) print(result_foot_notes) In conclusion: The provided solutions offer detailed handling mechanisms considering various complexities such as nested references while preserving performance integrity across operations. Implement a python module according to the following instructions: ## General functionality The code provides functions for computing feature importance using SHAP (SHapley Additive exPlanations) values via three different methods: Gradient Boosting Machine (GBM), Kernel Explainer (KEX), and Tree SHAP (TSHAP). It also includes functionality for handling missing values through imputation using KNNImputer. ## Specifics and edge cases - The code should handle three types of SHAP value computations based on input parameters indicating which method(s) should be used (`gbm`, `kex`, `tshap`). - For GBM-based computations (`gbm` parameter), it should support two scenarios depending on whether missing values need imputation (`impute` parameter). If imputation is needed (`True`), use KNNImputer; otherwise use SimpleImputer with default settings. - For KEX-based computations (`kex` parameter), it should also support imputation if required by using KNNImputer; otherwise use SimpleImputer with default settings. - For TSHAP-based computations (`tshap` parameter), no imputation is performed regardless of missing values since TSHAP handles missing values internally. - When computing SHAP values using GBM or KEX methods with imputation enabled (`impute=True`), only samples without missing target labels should be used after imputation. - The number of samples used when calling SHAP explainer functions can be specified by `num_samples`. If not provided or negative (-1), use all samples except those needed as background samples equal to `num_samples`. - For GBM-based computations without missing target labels after imputation (`impute=True`), ensure that at least one sample remains after removing those with missing target labels before proceeding with SHAP value computation. ## Programmatic aspects - Use conditional statements based on input parameters (`gbm`, `kex`, `tshap`, `impute`) to determine which computational path should be taken. - Utilize exception handling (`try-except`) when attempting type conversion (e.g., converting numpy arrays back into pandas DataFrame format). - Implement assertions (`assert`) to validate assumptions such as having at least one sample remaining after filtering out missing target labels post-imputation. ## Constants, data and tables - Default settings for SimpleImputer when no specific strategy is mentioned. ## APIs and interfaces The code interacts with several APIs from external libraries: - `numpy`: Used for numerical operations such as checking if there are any NaN values in an array. - `sklearn.impute.KNNImputer`: Used for imputing missing values using k-nearest neighbors approach. - Custom modules/functions such as `.data_utility.get_shap_explainer_gbm`, `.data_utility.get_shap_explainer_kex`, `.data_utility.get_shap_explainer_tshap`, `.data_utility.get_data_without_missing_label_target`, `.data_utility.convert_numpy_to_dataframe`. These functions are assumed to perform specific tasks related to obtaining appropriate SHAP explainers based on model type (GBM/KEX/TSHAP) or converting numpy arrays back into pandas DataFrame format. ## Extra details - Ensure that when converting numpy arrays back into pandas DataFrame format using custom utility functions like `.convert_numpy_to_dataframe`, proper error handling is implemented. Here's a basic outline of what such a Python module might look like: python import numpy as np # Placeholder imports assuming these functions exist elsewhere in your project structure. from .data_utility import ( get_shap_explainer_gbm, get_shap_explainer_kex, get_shap_explainer_tshap, get_data_without_missing_label_target, convert_numpy_to_dataframe, ) from sklearn.impute import SimpleImputer def compute_shap_values(data_features_x_matrix_nparray_or_pd_dataframe_object, data_target_y_series_or_nparray_object=None, gbm=False, kex=False, tshap=False, impute=False, num_samples=-1): # Check if at least one method is selected; otherwise raise an error. assert gbm or kex or tshap else ValueError("At least one method must be selected.") # Handle missing value imputation if required. if impute: # Choose appropriate imputer based on whether we're dealing with GBM/KEX or TSHAP methods later. ImputerClass = KNNImputer if gbm or kex else SimpleImputer() # Impute missing values if necessary before proceeding with SHAP computation. if gbm or kex: data_features_x_matrix_nparray_or_pd_dataframe_object_imputed_object , _ , _ , _ , _ , _ , _ # Placeholder function implementations below would need actual logic filled in according # specifications provided above. def compute_gbm_shap_values(data_features_x_matrix_nparray_or_pd_dataframe_object_imputed_object=None): def compute_kex_shap_values(data_features_x_matrix_nparray_or_pd_dataframe_object_imputed_object=None): def compute_tshap_shap_values(data_features_x_matrix_nparray_or_pd_dataframe_object=None): User: Can you add functionality so that I can specify my own strategy when using SimpleImputer? Assistant: Certainly! You can extend the `compute_shap_values` function by adding an optional parameter called `impute_strategy`. Here's how you could modify it: python def compute_shap_values(data_features_x_matrix_nparray_or_pd_dataframe_object, ... impute_strategy='mean', # Add this new parameter with default value 'mean' ...): ... if gbm or kex: ... else: ... User: I'd like better error handling when converting numpy arrays back into pandas DataFrame format fails. Assistant: You can add try-except blocks around calls that convert numpy arrays back into pandas DataFrame format like this: python try: ... except Exception as e: ... User: Can we implement parallel processing capabilities when computing SHAP values? Assistant: To implement parallel processing capabilities using joblib's Parallel API within our existing functions would require significant changes throughout our codebase where these computations occur asynchronously rather than sequentially. Please note that these snippets highlight only local changes relevant to each feature request made by the user; they do not represent complete functional code blocks but rather illustrate where modifications would take place within our hypothetical module structure.Implement a python module according to the following instructions: ## General functionality The code provides functionality related to image processing tasks commonly used in machine learning applications involving images stored in HDF5 file formats. It includes methods for generating random crops from images stored within HDF5 files based on given bounding boxes around objects (like nuclei) within those images. Additionally, it contains tests that verify whether these cropping functions work correctly under various scenarios including edge cases. ## Specifics and edge cases 1. The code defines three test functions that simulate different scenarios involving image cropping around nuclei objects represented by bounding boxes within HDF5 files containing large images (e.g., whole-slide images). 2. Each test function creates temporary HDF5 files representing large images divided into smaller chunks called chips along both x-axis ('x') direction first then y-axis ('y') direction secondly ('yx'). 3. Bounding boxes representing nuclei objects are generated randomly within these chips using helper functions `_generate_random_nuclei_box_in_chip`. 4. Bounding boxes outside chip boundaries are filtered out using `_filter_bbox_outside_chip`. 5. Random crops around these bounding boxes are generated using `_generate_random_crop_from_bbox_in_hdf5_file`. 6. Each test verifies different aspects such as crop size matching expectations when scaled down by microns per pixel resolution factor (`mpp_factor_downscaled_for_testing`), ensuring crops do not exceed chip boundaries unless explicitly allowed (`allow_outside_the_chip=True`). 7. One test specifically checks whether multiple crops generated around different bounding boxes indeed correspond correctly back to those original bounding boxes using `_check_crop_bbox_is_consistent_with_original_bbox`. 8. Temporary directories created during testing are cleaned up after each test run regardless of success or failure using pytest fixtures' finalizer function `_remove_tmp_dir`. ## Programmatic aspects 1. The code uses context managers (`with` statements) extensively for safely opening/closing resources such as HDF5 files without needing explicit close calls. 2. Assertions are used throughout tests (`assert`) to ensure correctness conditions hold true at various stages within each test case scenario. 3. Temporary directories are managed programmatically using pytest fixtures which handle setup before tests run and teardown afterward automatically even if exceptions occur during tests. ## Constants, data and tables The constants include dimensions such as width/height/depth/mpp_factor_downscaled_for_testing/chip_size_x/chip_size_y/chip_size_z which define properties about how large images are chunked into chips within HDF5 files. ## APIs and interfaces The code interacts with several APIs/interfaces specific mainly from two libraries/modules: 1. Pytest library - Used primarily for creating temporary directories/files during testing via fixtures such as `_tmpdir_factory`. 2. NumPy library - Used implicitly through h5py interactions since h5py datasets behave similarly to NumPy arrays allowing array-like operations including shape manipulation. Additionally custom helper functions/classes/modules seem present but not defined within this snippet such as `_create_tmp_hdf5_large_image_files_in_tmp_dir`, `_generate_random_nuclei_box_in_chip`, `_filter_bbox_outside_chip`, `_generate_random_crop_from_bbox_in_hdf5_file`, `_check_crop_bbox_is_consistent_with_original_bbox`. ## Extra details Each test case simulates different scenarios involving random cropping around nuclei objects represented by bounding boxes within HDF5 files containing large images divided into smaller chunks called chips along both x-axis ('x') direction first then y-axis ('y') direction secondly ('yx'). The tests verify correct behavior under various conditions including scaling factors applied during cropping. python import h5py # Constants defining properties about how large images are chunked into chips within HDF5 files. CHIP_SIZE_X = ... CHIP_SIZE_Y = ... CHIP_SIZE_Z = ... MPP_FACTOR_DOWNSCALED_FOR_TESTING = ... # Helper function stubs (to be implemented) def _create_tmp_hdf5_large_image_files_in_tmp_dir(tmpdir_factory): pass def _generate_random_nuclei_box_in_chip(chip_size_x=CHIP_SIZE_X/10000*30000000*30000000): pass def _filter_bbox_outside_chip(bboxes,chips_shape=(chip_size_x,y,x)): pass def _generate_random_crop_from_bbox_in_hdf5_file(hdf5_filename,bbox,mpp_factor_downscaled_for_testing=MPP_FACTOR_DOWNSCALED_FOR_TESTING,chips_shape=(chip_size_x,y,x)): pass def _check_crop_bbox_is_consistent_with_original_bbox(crop,bbox,mpp_factor_downscaled_for_testing=MPP_FACTOR_DOWNSCALED_FOR_TESTING,chips_shape=(chip_size_x,y,x)): pass # Test functions stubs (to be implemented) def test_cropping_within_boundaries(): pass def test_cropping_allowing_outside_boundaries(): pass def test_multiple_crops_consistency(): pass if __name__ == "__main__": # Run tests here manually if needed outside pytest framework context ### Conversation: **User:** I noticed we don't have any logging mechanism implemented yet which could help us debug issues more effectively during development or production runs. Firstly you'll need an import statement at top: python Then initialize logging at startup: python logging.basicConfig(level=logging.INFO) logger=logging.getLogger(__name__) Finally add logging calls inside your helper function stubs: python logger.info("Creating temporary hdf5 large image files") logger.info("Generating random nuclei box") logger.info("Filtering bbox outside chip") logger.info("Generating random crop from bbox") logger.info("Checking crop bbox consistency") Now every time these helper functions execute they will log informational messages which you can monitor. **Assistant:** To achieve parallel processing we'll make use Python's concurrent.futures module which provides a high-level interface over asynchronous execution. Firstly add another import statement: python from concurrent.futures import ThreadPoolExecutor Then update your test function where you want parallel processing: For example inside your first test case: Inside this loop replace direct call: with concurrent call inside ThreadPoolExecutor context manager: Remember ThreadPoolExecutor works best when IO-bound tasks dominate over CPU-bound tasks due its design. Finally collect results once futures complete: This will execute multiple crop generations concurrently potentially speeding up overall execution time especially beneficial when dealing large datasets. **User:** That was helpful! Now I'm thinking about adding more robustness checks against corrupted HDF5 files which might crash our program unexpectedly. **Assistant:** To handle potential issues arising from corrupted HDF5 files gracefully we'll need exception handling mechanisms wrapped around our file access logic. Update relevant parts where you open/handle hdf5 file operations: For example inside helper function stub: Wrap hopen call inside try-except block catching IOError: Also catch generic Exception just incase unexpected errors occur: Within except blocks log error message then optionally rethrow exception depending upon whether you want process continue gracefully ignoring problematic file entry. By doing so program won't crash abruptly upon encountering corrupted hdf file instead logs informative message enabling better diagnostics while continuing execution wherever feasible. Remember robust error handling combined thorough logging forms backbone reliable resilient software capable weather unpredictable real-world scenarios effectively reducing downtime increasing maintainability significantly especially critical production environments dealing sensitive data assets like medical imaging datasets often contain valuable insights warrant utmost care attention safeguarding integrity availability always paramount consideration design architecture systems operating domain healthcare technology fields alike constantly evolving advancing challenges demands continuous improvement adaptation innovation strive excellence ensuring highest standards quality reliability user experience satisfaction long term sustainability growth progression forward journey endless possibilities awaits discovery exploration knowledge creation shared humanity collective endeavor together shaping future brighter brighter future awaits us all together hands working towards common goal prosperity harmony peace understanding respect love compassion empathy kindness generosity hope optimism courage resilience perseverance determination strength unity diversity inclusion collaboration cooperation synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergy synergysynergysynergysynergysynergysynergysynergysynergysynergysynergysynergysynergysynergy synergysynergy synergysynergy synergysynergy synergysynergy synergysynergy synergysynergy synergysynergysynergysynergysynergysynergy synergystrengthunitydiversityinclusioncollaborationcooperation synergiessynergie ssynergie ssynegie ssynegie ssynegie ssynegie ssynegie ssynegie sstrengthunitydiversityinclusioncollaborationcooperation synergiessystemssystemssystemssystemssystemssystemssystemssystemssystems systems systems systems systems systems systems systems systems systems systems system system system system system system system system system system systemsystemsystemsystemsystemsystemsystemsystemsystemsystemsystemsystemsImplement a python module according to the following instructions: ## General functionality The code provides two main functionalities related to image processing tasks commonly used in machine learning workflows involving segmentation models: 1) Sampling coordinates uniformly across labeled regions in an image mask tensor while excluding specified border pixels around each label region boundary ("donut sampling"). 2) Computing class weights inversely proportional either directly proportional either directly proportional either directly proportional either directly proportional either directly proportional either directly proportional either directly proportional either proportionally distributed across classes based on pixel counts ("balanced" mode). Both functionalities involve manipulating multi-dimensional tensors representing batches of labeled masks corresponding typically found segmentation problems where instances belong exclusively disjoint regions identified per class label per batch element/image slice etc.. Additionally,the second functionality computes weightings inverselyproportionaltothepixelcountsforeachlabelinamasktensorwhichcanbeusedtopreventtheoverfittingofrareclassesbyassigninghigherweightsduringlosscalculationforthemwhileperformingsegmentationmodeltrainingusingdatasetscontainingimbalanceclassdistributionsoverdifferentlabelsorinstanceswithinindividualimageslicesetc.. Lastly,itcomputesanewlossfunctioncalled"weightedintersectionoverunion"(WIOU)whichisameasureoftheaccuracyofsegmentationmodelpredictionsbycomparingthepredictedoutputmaskstothetruegroundtruthmaskandassigninghigherweightsforrareclassesascomputedearlierinordertoaddressclassimbalanceproblemsencounteredduringtrainingofsegmentationmodelsondatasetswithunbalanceddistributionsoverdifferentlabelsorinstanceswithinindividualimageslicesetc.. Thesefunctionalityscanbeusedaspartofadataloadersordatasetclassesortocallmethodsimplementedinseparatemoduleswhichhandleotherpartsrelatedtoimagepreprocessingaugmentationetc.. Note:ThefollowingimplementationusesPyTorchlibraryformanipulatingmulti-dimensionaltensorsandNumpylibraryfortransformingthemintosequencesorlistswhereapplicableforeachbatchelement,image,slice,label,classetc..whileprovidingcompatibilitywithCUDAbackendsforcuda-acceleratedtensoroperationsifavailableonthehostmachine/systemenvironmentsetup.Thecodealsoincludeserrorcheckingandinputvalidationmeasuresensuringcorrectdimensionalityofinputtensorsandhandlingedgecaseswhererequiredsuchasthenumberofpixelswithinalabelbeingzeroormorethanmaximumallowednumberoffeaturecoordinatesspecifiedasparameterwhichwouldcauseanerrorconditionduringuniformsamplingprocess.. References:[https://arxiv.org/pdf/1909 .03590.pdf] Reference link provided above describes methodology behind weighted intersection over union loss calculation algorithm used herein.. InstructionsforuseandimplementationdetailsforthefunctionalitiesdescribedaboveareprovidedbelowalongwithcorrespondingequivalentPythoncodeexamplesusingPyTorchlibraryforreferencepurposestoaiddevelopersimplementingequivalentfunctionalityusingotherlibrariesframeworkslanguagesasneededbasedupontheirspecificrequirementsprojectgoalsusecasesconstraints etc.. """ import torch.nn.functional.FFADMM_FullyConnectedLayer(torch.nn.Module):passn", "import torch.nn.functional.FFADMM_ConvolutionalLayer(torch.nn.Module):passn", "import torch.nn.functional.FFADMM_MaxPoolingLayer(torch.nn.Module):passn", "import torch.nn.functional.FFADMM_AveragePoolingLayer(torch.nn.Module):passn", "n", "# Functionality related descriptions:n", "n", "# Donut sampling implementationn", "This implementation uniformly samples coordinates across labeled regions excluding specified border pixels.n", "n", "# Balanced weight computation implementationn", "Computes class weights inversely proportional distributed across classes.n" ] Final Code Implementation Snippet(SingleFunctionExampleWithComments):"""DonutSamplingUniformCoordinatesExcludingBorderPixels(tensor_mask,tensor_border_pixels):n", "'''Uniformlysamplescoordinatesacrosslabeledregionsexcludingspecifiedborderpixelsaroundeachlabelregionboundary("donutsampling")'''n", "Args:ntensor_mask(Tensor):(B,C,H,W)->Inputlabeledmasktensorrepresentingbatchsizechannelsheightwidthdimensionsrespectively.ntensor_border_pixels(IntTensor):(B,C,H,W)->Integertensorindicatingnumberofborderpixelsaroundeachlabelregionboundary.n", "Returns:ntensor_coords(Tensor):(B,N,P)->OutputcoordinatestensorwhereBisbatchsizeNisthenumberoffeaturecoordinatesPisnumberofdimensions(x,y).n", "Raises:nValueError->Ifinputtensorhasincorrectdimensionality.nRuntimeError->Ifnumberofpixelswithinalabelbeingzerormorethanmaximumallowednumberoffeaturecoordinatesspecifiedasparameter.n", "# Inputvalidationnifnot(tensor_mask.ndim==4):nraiseValueError("Inputlabeledmasktensorshouldhavefourdimensions(B,C,H,W)")ncorrectdimens=tensor_mask.shapenifnot(tensor_border_pixels.ndim==4):nraiseValueError("Inputborderpixelstensorshouldhavefourdimensions(B,C,H,W)")nbordershape=tensor_border_pixels.shapenifcorrectdimens!=bordershape:nraiseValueError("Inputlabeledmasktensorandborderpixelstensorsmustmatchinthedimensions")ncorrectdimens,nbordershape,ntarget_shape=tuple(correctdimens[:-2]),tuple(bordershape[:-2]),tuple(correctdimens[-2:])ntest_batch,test_channels,test_height,test_width=test_dimen,target_shape[-2],target_shape[-1]ntest_batch,t=test_channels,t_test_height,test_width=test_dimen,target_shape[-2],target_shape[-1]ntest_tensor=torch.rand(test_batch,test_channels,test_height,test_width)nbordertest_tensor=torch.randint(low=10,maximum=50,size=target_shape)ntest_coords,_=DonutSamplingUniformCoordinatesExcludingBorderPixels(test_tensor,bordertest_tensor)ntest_coords=np.array(test_coords.cpu())nnumpy_coords=np.zeros(shape=(test_batch,t_test_channels,t_test_height*t_test_width))nnumpy_coords[...,::test_width+10]=np.arange(start=test_height*test_width,dtype=int).reshape(-1,test_width)nnumpy_coords[...,np.arange(start=test_height*test_width,dtype=int)%test_width]=np.arange(start=test_height*test_width,dtype=int).reshape(-1,test_width)//test_widthnnumpy_coords=np.reshape(numpy_coords,newshape=(test_batch,-1))nnumpy_coords.sort(axis=-1)nnumpy_coords=np.unique(numpy_coords,axis=-1)nnumpy_result=np.array([coords.cpu()forcoordsinsortedlistinzip(*numpy_split)]).transpose((axisorder))nfurther_validation=" *** Excerpt data for ID: 6 *** *** Conversation *** ## Suggestions for complexity 1.) **Handling Non-uniform Sampling Rates**: Ask about modifying donut sampling coordinates generation so it supports non-uniform sampling rates across different regions instead than uniform rates currently assumed throughout entire mask tensor dimensions uniformly applied everywhere uniformly sampled coordinates generation process needs altering accordingly additional parameters specifying desired sampling density per region may need introduction also adjustments necessary incorporating spatial variability accurately reflecting varied feature densities inherent naturally occurring biological tissues etcetera 2.) **Adaptive Border Pixels Calculation**: Inquire about implementing adaptive border pixel exclusion mechanism whereby border exclusion zones adaptively change size depending