Removed unused imports (random, numpy, deque, OrderedDict)

[Montana]

I made several significant improvements to streamline and optimize the code. First, I removed redundant imports like random, numpy, and deque since they weren't being used in the codebase. Since Python 3.7+ maintains insertion order natively, I replaced the OrderedDict with a standard dict for parameter management. I optimized the parameter initialization using dictionary comprehension in the init_model function, making it more concise and efficient. I simplified the stride calculation logic for better readability and introduced nn.Sequential to organize the fully connected layers more effectively.

The code in itself is now more Pythonic by replacing constructs like if not log_stride is None with the more idiomatic if log_stride is not None. I reduced redundant variable assignments in the forward pass to improve clarity and performance. I streamlined the convolutional layer creation by using shared parameters and optimized the process_range method by combining operations. I improved memory efficiency through better code organization and structure. These changes collectively result in a more maintainable and performant implementation while preserving all the original functionality. The code now follows modern Python best practices and demonstrates cleaner architectural patterns.