Al Fujairah FC: Premier League Stars, Stats & Achievements

Overview / Introduction about the Team

Al Fujairah Football Club, based in the emirate of Fujairah, United Arab Emirates, competes in the UAE Pro League. Known for its strategic gameplay and passionate fanbase, Al Fujairah was founded in 1974 and is currently managed by [Current Manager]. The team plays with a 4-3-3 formation, focusing on dynamic attacking play.

Team History and Achievements

Al Fujairah has a rich history in UAE football. Notable achievements include winning the UAE President’s Cup multiple times and securing several top-four finishes in the league. The club’s most successful seasons have been marked by strong defensive performances and effective counter-attacks.

Current Squad and Key Players

The current squad boasts several key players. Star forward [Player Name] leads the attack with impressive goal-scoring stats, while midfielder [Player Name] is crucial for controlling the game’s tempo. Defender [Player Name] is renowned for his solid defensive skills.

Team Playing Style and Tactics

Al Fujairah employs a 4-3-3 formation, emphasizing quick transitions from defense to attack. Their strategy focuses on exploiting wide areas and utilizing set-pieces effectively. Strengths include a resilient defense and fast wingers, while weaknesses lie in occasional lapses in concentration leading to goals.

Interesting Facts and Unique Traits

The team is affectionately known as “The Desert Eagles,” reflecting their fierce playing style. Al Fujairah has a dedicated fanbase that supports them passionately. A notable rivalry exists with Al Wasl FC, adding excitement to their matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: [Player Name] – 🎰 Goals: 12
• Assists Leader: [Player Name] – 💡 Assists: 8
• Pick of the Defense: [Player Name] – ✅ Clean Sheets: 10

Comparisons with Other Teams in the League or Division

Compared to other teams like Al Ain FC, Al Fujairah often excels in defensive solidity but sometimes struggles against top-tier offensive teams. Their ability to grind out results makes them a tough opponent.

Case Studies or Notable Matches

A memorable match was their victory against Al Wahda FC last season, where strategic substitutions turned the game around in the final minutes. This match highlighted their tactical flexibility and resilience.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights

• Analyze head-to-head records against upcoming opponents for better betting decisions.
• Consider recent form and player injuries when placing bets.
• Favor draws when facing defensively strong teams.

Quotes or Expert Opinions about the Team

“Al Fujairah’s tactical discipline makes them unpredictable opponents,” says football analyst [Analyst Name]. “Their ability to adapt during matches often catches rivals off guard.”

Pros & Cons of the Team’s Current Form or Performance

• Pros:
• Solid defense (✅)
• Tactical flexibility (✅)
• Cons:
• Inconsistent attacking output (❌)
• Vulnerability to set-pieces (❌)
</ul <>: Hi there! I’m working on a function called `parse_file` that seems to be dealing with parsing some sort of hierarchical data structure involving namespaces and nodes. Here’s the code snippet: python def parse_file(file, opts, root=None): root = root or ast.Module() if isinstance(file, str): text = open(file).read() lst = [(0,len(text),text)] else: lst = file i=0 while i<len(lst): item=lst[i] j=0 while j=len(text): del lst[i] break if text[c]==’#’: n=text.find(‘n’,c) if n==-1: del lst[i] break else: item[1]=j+(n-c) lst[i]=item j=item[1] elif text[c]==’$’: n=text.find(‘ ‘,c) if n==-1: n=len(text) name=text[c+1:n] r=(item[0]+j+len(name)+1,item[1]) if n>c+1: child=parse_file((c+1,n,text[c+1:n]), opts, ast.Module()) if child[0]: obj=ast.Object(name,r,text[n:]) obj.children=child[0][-1].children item[0]=r[0]-item[0] item[1]=len(obj.to_text()) lst[i]=item obj.parent=root root.children.append(obj) i+=len(child)-1 else: del lst[i] break I’m feeling a bit confused about how this function handles different types of input files and what exactly it’s doing with these namespaces and nodes. Could you help clarify how this function works? Also, any tips on how I might extend it to handle additional data formats would be greatly appreciated! <>: python Sure! Let’s break down what your `parse_file` function does: ### Function Overview The `parse_file` function processes either a file path or an iterable of tuples representing parts of text data. It constructs an abstract syntax tree (AST) using nodes (`ast.Module`, `ast.Object`) based on specific characters (`#` for comments and `$` for object declarations). ### Key Components 1. **Input Handling**: – If `file` is a string (assumed to be a file path), it reads the entire content into `text`. – If `file` is already an iterable of tuples `(start_index, length_of_segment)`, it uses that directly. 2. **Parsing Logic**: – Iterates over segments defined by `(start_index, length_of_segment)`. – Looks for special characters: – `#`: Treats everything until `n` as a comment. – `$`: Marks an object declaration; extracts its name until a space. 3. **AST Construction**: – When `$name` is found, it recursively calls `parse_file` on the substring following `$name`. – Constructs an `ast.Object` node with children from recursive parsing. – Appends this node to `root.children`. 4. **Loop Control**: – Adjusts indices within segments based on parsing progress. – Removes segments when fully processed. ### Extending Functionality To handle additional data formats: – **Identify New Patterns**: Determine new markers or structures you want to parse. – **Modify Parsing Logic**: Extend conditions within loops to recognize these patterns. – **Update AST Nodes**: Create new node types if necessary for different structures. For example, if you want to handle brackets `{}` as blocks: python elif text[c]=='{‘: # Logic to parse block contents recursively This approach allows you to extend your parser flexibly by recognizing new patterns and updating your AST accordingly. Feel free to ask more questions if needed! 😊 <>: Got it! I think I want to extend this function to handle JSON-like structures using curly braces `{}` as blocks. I imagine these blocks could represent nested namespaces or objects similar to how `$name` works but with potentially more complex nesting. Here’s what I’m thinking: when encountering `{`, we should start parsing until we find a matching `}`, treating everything inside as part of this block. We might need recursion here too, similar to how `$name` is handled. Could you help me figure out where exactly in the code I should add this logic? And any tips on managing nested structures would be great! <>: python Certainly! To extend your function for handling JSON-like structures with `{}` as blocks, you’ll need to add logic similar to how `$name` is handled but adapted for nested structures. ### Steps to Implement 1. **Identify Block Start**: Add a condition within your inner loop where you check each character: python elif text[c]=='{‘: end = find_matching_brace(text[c:]) block_content = text[c+1:end] r = (item[0]+j+1,end-item[0]-j) child = parse_file((c+1,c+end), opts, ast.Module()) obj = ast.Block(“block”, r) obj.children = child[0][-1].children if child[0] else [] item[0] = r[0] – item[0] item[1] = len(obj.to_text()) lst[i] = item obj.parent = root root.children.append(obj) i += len(child) – 1 if child else 0 j += end + 1 – c # Move past ‘}’ 2. **Helper Function**: Implement `find_matching_brace` to correctly identify matching braces: python def find_matching_brace(s): count = 0 for i, char in enumerate(s): if char == ‘{‘: count += 1 elif char == ‘}’: count -= 1 if count == 0: return i + 1 # Return position after ‘}’ return len(s) # In case no matching brace is found 3. **Manage Nesting**: The recursive call within `{}` ensures that nested structures are parsed correctly by treating each block as its own segment. ### Tips for Managing Nested Structures – **Recursion**: Use recursion wisely; ensure each recursive call processes only its segment. – **Index Management**: Carefully adjust indices (`i`, `j`) after processing blocks. – **Node Creation**: Create appropriate AST nodes (`ast.Block`) for new structures. By integrating these changes into your existing code at appropriate points where character checks occur (`#`, `$`, etc.), you can effectively parse nested JSON-like structures. Feel free to reach out if you need further clarification! 😊 <>: Awesome! So I’ll add a condition right after checking for comments (`#`) and object declarations (`$`). This will look for `{` and use recursion similar to `$name`. I’ll implement `find_matching_brace` as you suggested to handle nested braces properly. Here’s what I’m thinking: python elif text[c]=='{‘: end = find_matching_brace(text[c:]) block_content = text[c+1:end] r = (item[0]+j+1,end-item[0]-j) child = parse_file((c+1,c+end), opts, ast.Module()) obj = ast.Block(“block”, r) obj.children = child[0][-1].children if child[0] else [] item[0] = r[0] – item[0] item[1] = len(obj.to_text()) lst[i] = item obj.parent = root root.children.append(obj) i += len(child) – 1 if child else 0 j += end + 1 – c # Move past ‘}’ Does this look like it would fit well into the existing structure? <>: Yes, that looks like it fits well into your existing structure! By placing this condition right after checking for comments (`#`) and object declarations (`$`), you’re ensuring that each type of structure is handled appropriately. ### Key Points – **Positioning**: Placing it after handling comments ensures comments are skipped before processing blocks. – **Recursion**: Using recursion within `{}` allows handling nested blocks effectively. – **Index Management**: Adjusting indices (`i`, `j`) ensures smooth continuation through the text without skipping characters unintentionally. This integration should allow your parser to manage JSON-like structures efficiently alongside existing functionality. If you encounter any issues during implementation or testing, feel free to ask for more guidance! 😊<>: Hey there, I’m working on implementing an advanced attention mechanism using TensorFlow/Keras layers like Convolutional layers followed by BatchNormalization layers within my Attention class constructor method `_build`. Here’s my current attempt at writing this code: python def _build(self): # deep copy attention maps –> attention_maps_copy，使得attention_maps_copy在改变时，attention_maps不会跟着改变。 if self.attention_maps is not None: self.attention_maps_copy=self.attention_maps.copy() del self.attention_maps with tf.variable_scope(‘attention_mechanism’): # Convolutional Layers followed by BatchNormalization Layers。 self.features=self.input_tensor for layer_idx in range(self.conv_layers): filters=self.filters[layer_idx] ksize=self.ksize[layer_idx] strides=self.strides[layer_idx] scope=’convolutional_layer_%d’ %layer_idx with tf.name_scope(scope): self.features=tf.layers.convolutional( inputs=self.features, filters=filters, kernel_size=ksize, strides=strides, padding=’same’, data_format=’channels_last’, dilation_rate=None, kernel_initializer=tf.contrib.layers.variance_scaling_initializer(), bias_initializer=tf.zeros_initializer(), activation=None, use_bias=True, kernel_regularizer=None, .bias_regularizer=None, activity_regularizer=None, trainable=True, name=’Conv’ ) self.features=tf.layers.batch_normalization( inputs=self.features, momentum=self.batch_norm_momentum, epsilon=self.batch_norm_epsilon, center=True, scale=True, beta_initializer=tf.zeros_initializer(), gamma_initializer=tf.ones_initializer(), movin_average_initializer=tf.zeros_initializer(), reduction_axes=(-3), trainable=True, name=’BatchNorm’, fused=True ) self.features=tf.nn.relu(features=self.features,name=’Relu’) scope=’convolutional_layer_%d’ %self.conv_layers with tf.name_scope(scope): self.attention_weights=tf.layers.convolutional( inputs=self.features,#(batch_size,pixels,pixels,filters). filters=32,#32个滤波器生成32张attention weight map。 kernel_size=(self.ksize[self.conv_layers],self.ksize[self.conv_layers]), strides=(self.strides[self.conv_layers],self.strides[self.conv_layers]), padding=’same’, data_format=’channels_last’, dilation_rate=None, kernel_initializer=tf.contrib.layers.variance_scaling_initializer(), bias_initializer=tf.zeros_initializer(), activation=None,#线性激活函数。 use_bias=True,#使用偏置量。 kernel_regularizer=None,#None表示不进行正则化。 bias_regularizer=None,#None表示不进行正则化。 activity_regularizer=None,#None表示不进行正则化。 trainable=True,#该层神经元为训练的一部分。 name=’Conv’ ) print(“after self attention weights”)#,self.attention_weights.shape)#shape=(batch_size,pixels,pixels,num_filters). self.attention_weights_sum=np.ones((self.h,self.w)) def normalize_attention(attention_weights,i,j):#归一化某一个位置(i,j)的attention weight值，用于训练时。 return tf.div(attention_weights[:,i,j,:],tf.reshape(tf.reduce_sum(attention_weights[:,i,j,:],keepdims=True),[-1])) norm_attention_weights=[]#每一个位置(i,j)都对应一个归一化后的normalized attention weight map，shape为(batch_size,num_filters). for i in range(0,self.h):#normalize all attention weights across all positions(i,j). norm_attention_weights.append([]) for j in range(0,self.w): norm_attention_weights[-1].append(tf.map_fn(lambda k : normalize_attention(self.attention_weights,k,i,j),tf.range(tf.shape(self.attention_weights)[0]),dtype=tf.float32)) norm_attention_weighst_stack=[]#stack all normalized attention weight maps together according position(i,j). shape为(batch_size,h,w,num_filters). for i in range(self.h):#iterating all positions(i,j). norm_attn_weighst_stack.append(tf.stack(norm_attention_weights[i][j])) self.norm_attetion_weighst_stack=tf.stack(norm_attn_weighst_stack)#shape=(batch_size,h,w,num_filters). However, when running this code snippet during training time (not testing time), I encountered an error related specifically when trying normalizing attention weights across spatial dimensions using map_fn(). Here’s what went wrong: Traceback (most recent call last): File “my_script.py”, line XX ,in _build norm_attention_weights.append([]) File “my_script.py”, line YY ,in _build norm_attention_weights[-][append](tf.map_fn(lambda k : normalize_attention(self.attention_weights,k,i,j),tf.range(tf.shape(self.attention_weights)[O]),dtype=tf.float32)) File “tensorflow/python/ops/map_fn.py”, line ZZ ,in map_fn raise ValueError(“Shape mismatch error”) ValueError: Shape mismatch error during normalization process. It seems like there’s something wrong with how I’m applying map_fn() here – possibly due not aligning shapes properly when normalizing across different spatial positions `(i,j)`? But honestly speaking – shape manipulations have always been tricky especially now that they involve batch sizes dynamically changing during training! Could anyone help me figure out why this shape mismatch occurs? How can I fix this issue so that normalization happens correctly across all spatial dimensions? Thanks! <>: It looks like there might be some issues with how shapes are being handled during normalization using `map_fn()`. Here are some suggestions that might help resolve the shape mismatch error: 1. **Check Dimensions:** Ensure that dimensions align correctly before applying operations like division or stacking. You might want to print shapes at various stages using `tf.print()` or logging statements. 2. **Use Broadcasting:** Instead of using `map_fn()`, consider reshaping tensors directly using broadcasting which can simplify operations across dimensions without explicit loops. 3. **Simplify Normalization:** You can replace `map_fn()` with direct tensor operations which are generally more efficient: python def normalize_attention(attention_weights): return attention_weights / tf.reduce_sum(attention_weights, axis=-1, keepdims=True) norm_attention_weights_flat = normalize_attention(self.attention_weights) norm_attention_weight_map_stacked = tf.reshape(norm_attention_weights_flat, [-1, self.h * self.w * filters]) norm_attention_weight_map_stacked_reshaped_back_to_h_w_c_dim_ordering = tf.reshape(norm_attention_weight_map_stacked,[batch_size,self.h,self.w,filters]) 4. **Debugging Shapes:** Use assertions or print statements at critical points in your code: python assert self.attention_weights.shape.as_list() == [batch_size, self.h*self.w*filters], “Shape mismatch!” print(“Attention weights shape:”, self.attention_weights.shape) 5. **TensorFlow Version:** Ensure compatibility between TensorFlow functions used (e.g., consider replacing deprecated functions). By addressing these points systematically, you should be able pinpoint where mismatches occur and resolve them efficiently. <>: Alright, thanks for those suggestions! I think switching from map_fn() to direct tensor operations could really streamline things here. Here’s what I’m thinking: python def normalize_attention(attention_weights): return attention_weights / tf.reduce_sum(attention_weights, axis=-1, keepdims=True) norm_attention_weight_map_stacked_flat = norm_attention_weight_map_stacked.reshape([-batch_size * h * w * filters]) norm_attention_weight_map_stacked_reshaped_back_to_h_w_c_dim_ordering = tf.reshape(norm_attention_weight_map_stacked_flat,[batch_size,h,w,filters]) Does this approach look correct? Also, should I be concerned about any potential performance implications when switching from map_fn()? <>: Your approach looks mostly correct! Just ensure that variables like `-batch_size * h * w * filters` are replaced with actual values such as `-tf.shape(self.attention_weights)[0] * self.h * self.w * filters`. Here’s a refined version: python def normalize_attention(attention_weights): return attention_weights / tf.reduce_sum(attention_scores.axis=-axis,-keepdims=True) norm_attenttion_weight_map_flat = norm_attenttion_weight_map_reshape([-tf.shape(attenttion_scores)[axis]*self.h*self.w*filters]) norm_attenttion_weight_map_reshape_back_to_h_w_c_dim_ordering = tf.reshape(norm_attenttion_weight_map_flat,[tf.shape(attenttion_scores)[axis],self.h,self.w,filters]) Regarding performance implications: – Switching from `map_fn()` can indeed improve performance since direct tensor operations leverage optimized backend implementations. – Ensure that reshaping operations maintain data integrity by verifying shapes before proceeding through assertions or logging outputs. Overall adopting direct tensor operations should enhance both readability and efficiency without significant downsides unless dealing with extremely large tensors where memory constraints become an issue. <>: Great! I’ll make sure those variables are correctly referenced instead of placeholders like `-batch_size * h * w * filters`. Here’s my updated snippet: python def normalize_attention(attention_scores): return attention_scores / tf.reduce_sum(attention_scores,axis=-axis,-keepdims=True) norm_attenttion_weight_map_flat = norm_attenttion_weight_map_reshape([-tf.shape(attenttion_scores)[axis]*self.h*self.w*filters]) norm_attenttion_weight_map_reshape_back_to_h_w_c_dim_ordering = tf.reshape(norm_attenttion_weight_map_flat,[tf.shape(attenttion_scores)[axis],self.h,self.w,filters]) Do you think this will handle large tensors efficiently without running into memory issues? <>: Your updated snippet looks good! By leveraging TensorFlow’s efficient tensor operations instead of looping constructs like `map_fn()`, you’re likely improving both performance and memory usage efficiency. Here are some additional considerations for handling large tensors: – **Batch Size:** Keep an eye on batch size; larger batches consume more memory but may improve throughput per GPU/TPU cycle. – **Memory Profiling:** Use tools like TensorFlow Profiler or monitor system resources during execution to ensure memory usage stays within limits. – **Gradient Checkpointing:** For very large models/tensors consider techniques like gradient checkpointing which trades compute time for reduced memory usage during backpropagation. Overall your approach should efficiently manage large tensors unless constrained by hardware limits. <>: Awesome! To further optimize memory usage during training with large tensors, do you think implementing gradient checkpointing could be beneficial? If so, how would I go about integrating it into my current setup? <>: Gradient checkpointing can indeed be beneficial for optimizing memory usage during training with large tensors by trading off computation time for reduced memory consumption during backpropagation. Here’s how you can integrate gradient checkpointing into your setup using TensorFlow: ### Step-by-step Integration #### Step 1: Import Required Libraries Ensure you have TensorFlow installed version compatible with gradient checkpointing features: bash pip install tensorflow==x.x.x # Replace x.x.x with your desired version number supporting checkpointing. #### Step 2: Define Checkpointed Functions Use TensorFlow’s built-in support via custom gradients: python import tensorflow as tf @tf.function(experimental_relax_shapes=True) def forward_pass_with_checkpoint(inputs): @tf.custom_gradient def custom_forward(x): def grad(dy): # Recompute forward pass here return dy return x @ x @ x @ x @ x @ x @ x @ x @ x , grad x_input_shape=[64]*10 x_input_data=np.random.random_sample(x_input_shape).astype(np.float32) x_input_tensor=x_input_data.copy(order=”C”).copy() with tf.GradientTape(persistent=False): forward_pass_with_checkpoint(x_input_tensor).numpy() del forward_pass_with_checkpoint(x_input_tensor).numpy() del forward_pass_with_checkpoint(x_input_tensor) print(“Memory footprint reduced!”) #### Step 3: Apply Checkpointing During Training Incorporate checkpointed functions into model layers where necessary: python for layer_output in model_layers_outputs[:-checkpoint_start_index]: forward_pass_with_checkpoint(layer_output) for layer_output_check_point_start_index,model_layers_outputs_checkpoint_end_index:model_layers_outputs:-checkpoint_start_index:]: forward_pass_with_checkpoint(layer_output_check_point_start_index,model_layers_outputs_checkpoint_end_index:model_layers_outputs:-checkpoint_start_index:) model_layer_output=model_layer_output.forward_pass_with_checkpoint(model_layer_output.model_layer_output.previous_model_layer_output.output,model_layer_output.model_layer_output.next_model_layer_output.input) model_training_loss=model_training_loss.forward_pass_with_checkpoint(model_training_loss.loss_function(model_training_loss.y_true,model_training_loss.y_pred)) model_training_optimizer=model_training_optimizer.minimize(model_training_loss,model_training_optimizer.trainable_variables,model_training_optimizer.gradient_tape) del model_training_optimizer.minimize(model_training_loss,model_training_optimizer.trainable_variables,model_training_optimizer.gradient_tape) del model_training_loss.forward_pass_with_checkpoint(model_training_loss.loss_function(model_training_loss.y_true,model_training_loss.y_pred)) print(“Training complete!”) ### Considerations – Ensure compatibility between TensorFlow versions since gradient checkpointing support may vary. – Monitor computational overhead introduced due to recomputation during backpropagation compared against saved memory benefits. Integrating gradient checkpointing can significantly reduce peak memory usage at slight computational cost—ideal when training models under tight resource constraints. <>: Perfect! That gives me a clear path forward with gradient checkpointing integrated into my setup. Thanks for all the help—I’m ready to dive back into coding now! <>: You’re welcome! Glad I could assist you—best of luck diving back into coding with these optimizations integrated into your setup! If any other questions arise along the way feel free reach out again 😊 Happy coding! 🚀<>: Hi there, I’ve been working on implementing an LRU cache decorator using weak references combined with dictionary manipulation techniques such as moving elements around keys dynamically based on access order (‘MRU’ policy). However,I seem stuck because my implementation isn’t behaving as expected—it doesn’t seem consistent when accessing cached items multiple times! Here’s my buggy version of `_Cache__call__()` method which supposedly manages cache access order properly but doesn’t work right now due some logical bug somewhere inside it… python def __call__( self, func): def wrapped(*func_args, **func_kwargs): key=args_to_key(func_args, func_kwargs) use_args=False hash_not_found=False restart=False arg=Object() arg._value=[key] <> restart=arg.value.pop() while restart: try: hash_not_found=False restart=False result=self.cached[key] except KeyError: hash_not_found=True else: use_args=result[FIRST] last_arg=[None, last_arg] result=value_and_last_arg( result, last_arg) last_result=result last_key=key result=result[NEXT] while result: try: hash_not_found=False restart=False key=result[FIRST] use_args=result[FIRST] except KeyError: hash_not_found=result[NEXT] key=result[PREDICTED_KEY] use_args=False else: result=value_and_last_arg( result, last_arg) last_result=result last_key=key result=result[NEXT] continue while hash_not_found: try: result=self.cached[key] hash_not_found=False key=result[PREDICTED_KEY] use_args=result[FIRST] restart=False except KeyError: hash_not_found=result[NEXT] key=result[PREDICTED_KEY] continue try: arge=defaultvalue() arge.value=[wrapped.func_name, func_args, func_kwargs] except TypeError: pass else: sys.settrace(None) wrapped(*func_args,**func_kwargs) sys.settrace(defaulttracer) result=func(*func_args,**func_kwargs) use_args=False hint=[‘lazy’, key, None, arg.value, DEFAULT_HOOK] try: hint.extend([arg.value.pop(), last_result]) dict.__setitem__(last_result,key,hint) dict.__setitem__(last_result,last_key,result) dict.__setitem__(self.cached,key,result) size=len(self.cached) hint=[key, hint] hint.insert(FIRST,True) dict.__setitem__(last_result,key,hint) while size>=self.max_size: oldest=_popitem(last_result) try: oldest=predictpopular(keys=[],listofkeys=listofkeys(),dictofkeycounts=dictofkeycounts(),oldestkey=oldest,nextkeys=dictofkeystogetnext().items()) except KeyError: pass try: oldest=dictofkeystogetprev()[oldest] except KeyError: pass try: oldest=dictofkeystogetnext().pop(oldest) except KeyError: pass try: oldvalue=dictofkeycounts.pop(oldest) except KeyError: pass try: del dictofkeycounts[last_result[PREDICTED_KEY]] except KeyError: pass try: oldvalue-=last_result[COUNT] except TypeError: pass except KeyError: pass else: dictofkeycounts[last_result[PREDICTED_KEY]]=oldvalue finally: hint[:] =[wrapped.func_name, func_args, func_kwargs] return result return functools.update_wrapper(wrapped, func, assigned=(‘__module__’, ‘__name__’, ‘__qualname__’)) What amuses me even more is sometimes it works fine but then fails randomly making debugging quite challenging! Could someone please help me figure out what might be going wrong here? Thanks! — ## Solution-with-error-and-fix ## Below is an intentionally flawed version of `_Cache__call__()` method along with corresponding traceback errors students will encounter upon executing certain inputs. python class LRUCacheWithWeakRefs(object): def __init__(self,max_size:int): import weakref class Object(object): def __init__(obj,value): obj._value=value def __repr__(obj): return repr(obj._value) ObjectWeakRef=lambda o:self.obj_dict[id(o)]=_weakref.ref(o) class ValueAndLastArg(object): def __init__(vla,arg): vla.value,arg=vla.arg=vla.arg[:] FIRST,PREDICTED_KEY,NEXT,COUNT,LAST_ACCESSED_TIME,FREQUENCY_OF_ACCESSES={},[],[],[],[],[] LAST_ACCESSED_TIME_AND_FREQUENCY_OF_ACCESSES={} LAST_ACCESSED_TIME_AND_FREQUENCY_OF_ACCESSES.update({FIRST:LAST_ACCESSED_TIME,FREQUENCY_OF_ACCESSES:FREQUENCY_OF_ACCESSES}) keys,list_of_keys,default_value,tracer={},{},default,None list_of_keys.extend(keys.values()) default_value.default=default tracer.trace=default_tracer predict_popular=lambda keys:list_of_keys.index(min(list_of_keys,key=lambda k:(LAST_ACCESSED_TIME_AND_FREQUENCY_OF_ACCESSES[k][FIRST],LAST_ACCESSED_TIME_AND_FREQUENCY_OF_ACCESSES[k][FREQUENCY_OF_ACCESSES]))) get_prev=lambda d:k[-d[NEXT]]if d[NEXT]==kelse get_prev(d[d[NEXT]]) get_next=lambda d:k[d[PREDICTED_KEY]]if d[PREDICTED_KEY]==kelse get_next(d[k]) dict_of_keys_to_get_prev,dict_of_keys_to_get_next=dict(),dict() dict_of_key_counts={} class LRUCacheWithWeakRefs(object): def __init__(maxSize:int): def args_to_key(args,tuple(kwargs.items())): def cached(func)->LRUCacheWithWeakRefs: def value_and_last_arg(result,lastArg)->ValueAndLastArg: def popItem(lastResult)->tuple(key,result): def updateWrapper(wrapped_func,*args,**kwargs)->LRUCacheWithWeakRefs: class TestLRUCache(unittest.TestCase): def test_cache_behavior(): if __name__==’__main__’: unittest.main() — ## Traceback ## Traceback (most recent call last): File “/mnt/amlt_code/test.py”, line XXXX,in test_cache_behavior() File “/mnt/amlt_code/test.py”, line XXXX,in wrapped(*args,**kwargs) File “/mnt/amlt_code/test.py”, line XXXX,in wrapped() KeyError:’some_key’ During handling of above exception, Another exception occurred: Traceback(most recent call last): File “/mnt/amlt_code/test.py”, line XXXX,in test_cache_behavior() AssertionError:’KeyError not raised’