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• What time do matches typically start?
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  Yes! Enjoy snacks like hot dogs & popcorn along with beverages like soft drinks & beer throughout game days!
• Where can I buy team merchandise?
  Visit official team stores located within stadiums or order online via our official store link! [0]: """ [1]: This file contains functions used by various parts of this package. [2]: Author : Sourangshu Bhattacharya [3]: """ [4]: import numpy as np [5]: def _is_int(x): [6]: """Check if x is integer""" [7]: return x == int(x) [8]: def _check_1d_inp(array): [9]: """Check if array is one-dimensional""" [10]: if not array.ndim ==1: [11]: raise ValueError("Array should be one-dimensional") [12]: def _check_equal_lengths(array_list): [13]: """Check if all arrays have equal lengths""" [14]: if len(set([len(array)for array in array_list])) !=1 : [15]: raise ValueError("All input arrays should have equal length") [16]: def _check_equal_length_2d_inp(array_list): [17]: """Check if all arrays have equal lengths""" [18]: if len(set([array.shape[0]for array in array_list])) !=1 : [19]: raise ValueError("All input arrays should have equal length") [20]: def _check_n_clusters(n_clusters): [21]: """Check if n_clusters is valid""" [22]: if not _is_int(n_clusters): [23]: raise TypeError("n_clusters should be integer") [24]: elif n_clusters <=0 : [25]: raise ValueError("n_clusters should be greater than zero") ***** Tag Data ***** ID: N1 description: Function `_check_equal_length_2d_inp` checks if all arrays in `array_list` have equal first dimension lengths using list comprehensions combined with set operations, which requires understanding both list comprehensions and set theory. start line: 16 end line: 19 dependencies: - type: Function name: _check_equal_length_2d_inp start line: 16 end line: 19 context description: This function ensures consistency among multiple input arrays' dimensions which is critical in machine learning preprocessing stages. algorithmic depth: 4 algorithmic depth external: N obscurity: 2 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************* ## Suggestions for complexity 1. **Add support for multidimensional arrays**: Modify the function so that it checks not only the first dimension but also other specified dimensions across all arrays. 2. **Integrate logging**: Add detailed logging capabilities that log mismatches along with array indices where mismatches occur. 3. **Handle nested lists**: Modify the function so that it can handle nested lists (lists within lists) while still checking consistency across dimensions. 4. **Include tolerance levels**: Allow small variations within specified tolerance levels between array lengths rather than enforcing strict equality. 5. **Parallel processing**: Enhance performance by using parallel processing techniques when checking large numbers of arrays. ## Conversation <|user|>[SNIPPET]<|end|> <|assistant|>Got it! How can I help you with this code? Do you need an explanation or some modifications?<|end|> <|user|>I want check not only first dimension but also second dimension too<|end|> <|assistant|>Understood! To check both the first and second dimensions across all arrays, we can modify your function as follows: python def _check_equal_length_2d_inp(array_list): """Check if all arrays have equal first and second dimensions""" first_dims = {array.shape[0] for array in array_list} second_dims = {array.shape[1] for array in array_list} if len(first_dims) !=1 : raise ValueError("All input arrays should have equal length along first dimension") if len(second_dims) !=1 : raise ValueError("All input arrays should have equal length along second dimension") This modification checks both dimensions separately.<|end|> <|user|>what if list inside list? need check dims inside too<|end|> <|assistant|>To handle nested lists while checking dimensions inside too, we'll need a recursive approach: python def _check_equal_length_nested(inp): """Helper function to check dimensions recursively.""" # If input is list of lists (nested) if isinstance(inp[0], list): first_dims = {_check_equal_length_nested(sublist) for sublist in inp} return next(iter(first_dims)) # return one common dimension # If input is ndarray-like (assumed) else: return inp.shape def _check_equal_length_2d_inp(array_list): """Check if all nested lists have equal dimensions.""" dims = {_check_equal_length_nested(array) for array in array_list} # Check consistency across all checked dimensions if any(isinstance(dim,(tuple,list)) for dim in dims): flat_dims = {dim[i] for dim_set in dims for dim in dim_set} if len(flat_dims) !=1 : raise ValueError