Odishi 1919: Champions of the Georgian League - Squad, Stats & Achievements

Overview / Introduction about the Team

The Odishi 1919 football team hails from Georgia, competing in the top-tier league of Georgian football. Established in 1919, the team is renowned for its rich history and passionate fanbase. Under the leadership of their current coach, they have consistently demonstrated strong performances in league matches.

Team History and Achievements

Odishi 1919 has a storied history with numerous titles and accolades. They have won multiple league championships and have been consistent top performers over the years. Notable seasons include their championship win in 2018, which solidified their status as a dominant force in Georgian football.

Current Squad and Key Players

The current squad boasts several key players who are pivotal to their success. Star forward Giorgi Papunashvili is known for his goal-scoring prowess, while midfielder Lasha Shengelia provides exceptional playmaking abilities. Defender Davit Chakvetadze is a cornerstone of their defense.

Team Playing Style and Tactics

Odishi 1919 typically employs a 4-3-3 formation, focusing on attacking play with quick transitions. Their strengths lie in their offensive strategies and ability to maintain possession. However, they occasionally struggle with defensive consistency.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “The Warriors” by their fans. They have a fierce rivalry with Dinamo Tbilisi, which often draws large crowds. Traditions include pre-match rituals that involve fan chants echoing through the stadium.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Giorgi Papunashvili – ✅
• Pivotal Midfielder: Lasha Shengelia – 💡
• Defensive Anchor: Davit Chakvetadze – 🎰
• Average Goals per Match: 1.8 – ✅
• Average Possession: 58% – 💡

Comparisons with Other Teams in the League or Division

In comparison to other teams in the Georgian Premier League, Odishi 1919 stands out for their balanced attack and solid midfield control. While teams like Torpedo Kutaisi focus more on defensive strategies, Odishi emphasizes offensive play.

Case Studies or Notable Matches

A breakthrough game was their victory against FC Samtredia in 2019, where they secured a crucial win that propelled them into playoff contention. This match highlighted their tactical flexibility and resilience under pressure.

Tips & Recommendations for Analyzing the Team or Betting Insights (💡 Advice Blocks)

• Analyze recent form: Focus on games from the last five matches to gauge momentum.
• Evaluate head-to-head records: Historical performance against upcoming opponents can provide insights into potential outcomes.
• Leverage player statistics: Key players’ performance trends can influence game predictions.

“Odishi’s tactical acumen makes them unpredictable yet formidable opponents,” says football analyst Irakly Kereselidze.

Mandatory Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

• ✅ Strong offensive lineup capable of scoring multiple goals per match.
• ❌ Inconsistent defense leading to occasional lapses against strong teams.
• ✅ High possession stats indicating control during matches.
• ❌ Struggles against top-tier European teams when participating in international competitions.</li
  <>: Hi there! I’m working on this code snippet that processes text lines using regular expressions to extract various elements like dates, names, locations, etc., from different types of documents such as logs or reports.

  Here’s the snippet:

  python
  def __init__(self):
  self.compiutere = re.compile(‘d{6}’)
  self.name = re.compile(‘[w+]’)
  self.location = re.compile(‘d{5}’)
  self.mesaj = re.compile(‘[a-zA-Z]+’)
  def __get_info(self,text):
  staff_id = re.findall(self.compiutere,text)
  name_result = re.findall(self.name,text)
  name = [item[1:-1]for item in name_result]
  location_result = re.findall(self.location,text)
  location = [item[:-1]for item in location_result]
  mesaj = self.mesaj.findall(text)
  data_staff = {}
  for index,item in enumerate(staff_id):
  data_staff[item]={‘nume’:name[index],’locatie’:location[index]}
  return data_staff

  I’m feeling a bit confused about how this `__get_info` method works within this class structure. It seems like it should be part of some larger functionality but isn’t quite complete or integrated properly. Could you help me understand how I might correctly implement this method so it effectively extracts and returns structured information? Thanks!
  <>: python
  Sure! It looks like your `__get_info` method is intended to extract information from text using regular expressions but isn’t fully integrated into your class structure. Here’s how you can adjust it:

  1. Make `__get_info` an instance method by removing double underscores.
  2. Ensure it uses `self` to access class attributes.
  3. Return structured data properly.

  Here’s a revised version:

  python
  import re

  class TextProcessor:
  def __init__(self):
  self.compiutere = re.compile(r’d{6}’)
  self.name = re.compile(r'[(w+)]’)
  self.location = re.compile(r'(d{5})’)
  self.mesaj = re.compile(r'[a-zA-Z]+’)

  def get_info(self, text):
  staff_id_matches = self.compiutere.findall(text)
  name_matches = self.name.findall(text)
  location_matches = self.location.findall(text)

  data_staff = {}

  for index, staff_id in enumerate(staff_id_matches):
  if index < len(name_matches) and index < len(location_matches):
  data_staff[staff_id] = {
  'nume': name_matches[index],
  'locatie': location_matches[index]
  }

  return data_staff

  # Example usage:
  processor = TextProcessor()
  text_example = "[John]123456 somewhere 12345 more text"
  info = processor.get_info(text_example)
  print(info)

  This version ensures that `get_info` is properly integrated into your class and handles potential mismatches between extracted lists by checking indices before accessing them. 😊

  <>: Great! Now let’s make sure we’re capturing all possible messages too. I think we should iterate over each match found by `mesaj` and store them separately along with other extracted data. How can we adjust the code to handle multiple messages effectively?
  <>: python
  Certainly! To capture all possible messages alongside other extracted data, you can modify the `get_info` method to store messages separately for each entry in `data_staff`. Here’s how you can do it:

  python
  import re

  class TextProcessor:
  def __init__(self):
  self.compiutere = re.compile(r’d{6}’)
  self.name = re.compile(r'[(w+)]’)
  self.location = re.compile(r'(d{5})’)
  self.mesaj = re.compile(r'[a-zA-Z]+’)

  def get_info(self, text):
  staff_id_matches = self.compiutere.findall(text)
  name_matches = self.name.findall(text)
  location_matches = self.location.findall(text)

  # Find all words as potential messages
  message_matches = [match.group() for match in self.mesaj.finditer(text)]

  data_staff = {}

  for index, staff_id in enumerate(staff_id_matches):
  if index < len(name_matches) and index < len(location_matches):
  # Store messages associated with each staff ID entry
  messages_for_entry = [msg for msg in message_matches if msg not already stored]
  data_staff[staff_id] = {
  'nume': name_matches[index],
  'locatie': location_matches[index],
  'mesaje': messages_for_entry
  }

  return data_staff

  # Example usage:
  processor = TextProcessor()
  text_example = "[John]123456 somewhere 12345 more text another message"
  info = processor.get_info(text_example)
  print(info)

  This approach ensures that each entry includes any relevant messages found within the text alongside other extracted details. Adjust the logic inside `messages_for_entry` as needed to ensure correct association with entries if required by your specific use case! 😊

  <>: Alright! Let’s refine how we associate messages with each entry. We need a way to ensure that messages are linked correctly to specific entries based on context or proximity within the text. Maybe we can use some logic to determine which messages belong to which entry? Any ideas on how to implement this?
  <>: python
  Certainly! To associate messages correctly with each entry based on context or proximity, you can use indices from your regex matches to group related information together. Here’s one way to implement this:

  python
  import re

  class TextProcessor:
  def __init__(self):
  self.compiutere_regexs=re.compile(r’bd{6}b’)
  name_regexs=re.compile(r'[(w+)]’)
  loc_regexs=re.compile(r’bd{5}b’)
  mesaj_regexs=re.compile(r’b[a-zA-Z]+b’)

  def get_info(self,text):

  staff_ids=self.compiutere_regexs.finditer(text)
  names=self.name_regexs.finditer(text)
  locs=self.loc_regexs.finditer(text)
  msgs=self.mesaj_regexs.finditer(text)

  # Convert iterators to lists of tuples (match object, start index)
  staff_ids=[(m,m.start()) for m in staff_ids]
  names=[(m,m.start()) for m in names]
  locs=[(m,m.start()) for m in locs]
  msgs=[(m,m.start()) for m in msgs]

  # Sort all matches by start index
  all_items=sorted(staff_ids+names+locs+msgs,key=lambda x:x[1])

  data_staff={}
  current_messages=[]

  # Iterate through sorted items by position
  for item_type,item_index_start,item_obj_index_start_item_obj_index_end_item_obj_group_tuple_item_obj_group_tuple_item_obj_index_end_item_obj_group_tuple_item_obj_index_end_item_obj_group_tuple_item_obj_group_tuple_ignores_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end_in_range_of_current_messages_start_current_messages_end(item_type,item_index_start,(item_obj_index_start,(item_obj_index_end,(item_obj_group_tuple,(ignore_ranges,current_message)))))):
  _,start_pos,_=item_type
  if item_type[:1]==staff_ids:
  current_message=[]
  if len(current_message)>0:
  current_message.append((current_message,”))
  current_entry=(str(item_obj_group_tuple),”,[],[])
  data_staff[str(item_obj_group_tuple)]={}
  current_entry=data_staff[str(item_obj_group_tuple)]
  elif item_type[:1]==names:
  current_entry[‘nume’]=str(item_obj_group_tuple)
  elif item_type[:1]==locs:
  current_entry[‘locatie’]=str(item_obj_group_tuple)
  elif item_type[:1]==msgs:
  if ignore_ranges==False:
  current_message.append(str(item_obj_group_tuple))
  else :
  pass

  # Check if message range ends here
  ignore_ranges=False
  if len(current_message)>0 :
  end_pos=start_pos+len(str(item_obj_group_tuple))
  _current_message=current_message.copy()
  _current_message.append((end_pos,))
  ignore_ranges=True
  _current_message=_current_message[:-1]

  # Add message range info
  current_entry[‘mesaje’].append(_current_message)

  return data_staff

  # Example usage:
  processor=TextProcessor()
  text_example=”[John]123456 somewhere 12345 more text another message”
  info=processor.get_info(text_example)

  print(info)

  This implementation uses indices from regex matches to group related information together based on proximity within the text. Adjustments may be needed depending on specific requirements or edge cases! 😊

  <>: This looks promising! Let’s refine it further by ensuring that messages are grouped based on proximity within a certain threshold distance from each entry (like staff ID). We could define a maximum distance within which a message should be considered part of an entry’s context. How can we implement this logic efficiently?
  <>: python
  Certainly! To group messages based on proximity within a certain threshold distance from each entry like a staff ID, you can introduce a distance check when associating messages with entries:

  python
  import re

  class TextProcessor:
  def __init__(self):
  self.staff_regexes=re.compile(r’bd{6}b’)
  name_regexes=re.compile(r'[(w+)]’)
  loc_regexes=re.compile(r’bd{5}b’)
  msg_regexes=re.compiled(b'[a-zA-Z]+b’)

  def get_info(self,text,max_distance=50):

  staff_ids=self.staff_regexes.finditer(text)
  names=self.name_regexes.finditer(text)
  locs=self.loc_regexes.finditer(text)
  msgs=self.msg_regexes.finditer(text)

  # Convert iterators to lists of tuples (match object, start index)
  staff_ids=[(m,m.start()) for m in staff_ids]
  names=[(m,m.start()) for m in names]
  locs=[(m,m.start()) for m in locs]
  msgs=[(m,m.start()) for m in msgs]

  # Sort all matches by start index
  all_items=sorted(staff_ids+names+locs+msgs,key=lambda x:x[1])

  data_staff={}

  # Iterate through sorted items by position
  current_entry=None

  for item_type,item_index_start,item_match_object,item_match_object_group,start_pos,end_pos,_in range(all_items):

  if item_type==staff_ids :
  current_entry={‘nume’:”,’locatie’:”,’mesaje’:[]}
  data_staff[str(item_match_object.group())]=current_entry

  elif item_type==names :
  current_entry[‘nume’]=str(item_match_object.group())

  elif item_type==locs :
  current_entry[‘locatie’]=str(item_match_object.group())

  elif item_type==msgs :

  if not any(abs(start_pos-msg[0])<=max_distance for msg,msg_list,current_msg_list_length,index_msg_list_length,in range(len(current_entry['mesaje']))):

  current_msg_list=[]
  end_pos=start_pos+len(str(item_match_object.group()))
  _current_msg_list=current_msg_list.copy()
  _current_msg_list.append((end_pos,))
  ignore_ranges=True
  _current_msg_list=_current_msg_list[:-1]

  # Add message range info
  current_entry['mesaje'].append(_current_msg_list)

  return data_staff

  # Example usage:
  processor=TextProcessor()
  text_example="[John]123456 somewhere 12345 more text another message"
  info=processor.get_info(text_example,max_distance=50)

  print(info)

  This implementation checks whether each message is within a specified maximum distance (`max_distance`) from an entry like a staff ID before associating it with that entry's context. Adjust `max_distance` as needed based on your specific requirements! 😊

  <>: Awesome! This should work well for my needs now. I’ll test it out with some sample texts and see how it performs. Thanks for the help!
  <>: You’re welcome! I’m glad I could help set up your solution. Feel free to reach out if you encounter any issues or need further adjustments while testing with your sample texts. Happy coding! 😊<>: Hey there,

  I’m trying to use this function `_parse_input` which is supposed to parse input parameters using AST parsing techniques combined with dynamic evaluation via `eval`. The idea is that it evaluates expressions safely without executing arbitrary code directly.

  Here’s my buggy version of the code:

  python
  import ast

  def _parse_input(path):
  “”” parses path according following syntax rules
  path <- here | cd '’ | cd “” | cd ../intreface_.json | cd ../../interface_.json”””
  tokens=pyparsing.Word(pyparsing.alphanums+’._-‘).parseString(path).asList()
  interface=None;
  path=None;
  while tokens:!=[]
  token=tokens.pop(0);
  if token==’here’:
  return os.path.abspath(os.curdir);
  elif token==’cd’:
  path=tokens.pop(0);
  if path[0]==”‘”:
  path=path[1:-1];
  interface=None;
  elif token==’interface’:
  interface_name=tokens.pop(0);
  modifcator=”;
  if len(tokens)>0 and tokens[0]==’.’:
  tokens.pop(0);
  if len(tokens)>0;
  modifcator=tokens.popp(0);
  abs_path=_find_abs_path(interface_name+modifcator);
  interface_path=os.path.join(abs_path,’interface_’+interface_name+’.json’);
  print(‘found interface at ‘+interface_path);
  interface=_load_interface(interface_path);
  return os.path.join(path or os.curdir , ‘interface_’+interface_name+’.json’);

  def _find_abs_path(interface_name_modifcator):
  return “/mock/path”

  def _load_interface(interface_path):
  return {“mock”: “data”}

  # Simulating calling function.
  result=_parse_input(“cd ‘./some_folder'”)
  print(result)

  When I run this code snippet above using Python 3.x interpreter I am getting an error traceback but I don’t quite understand what went wrong here.

  Here’s what I’m seeing:

  Traceback (most recent call last):
  File “parser.py”, line 33, in
  result=_parse_input(“cd ‘./some_folder'”)
  File “parser.py”, line 13, in _parse_input
  path=path[1:-1];
  TypeError: ‘NoneType’ object is not subscriptable

  It seems like something goes wrong when handling paths starting with `’`. But I’m not sure why `path` becomes None here when trying slice it.

  Could someone please help me figure out what’s going wrong?

  Thanks!

  <>: python
  The error occurs because when `tokens.pop(0)` doesn’t find anything after `’cd’`, it returns `None`, leading to attempting slicing on `None`.

  Here’s how you can fix it:

  python
  import os

  def _parse_input(path):
  “”” parses path according following syntax rules
  path <- here | cd '’ | cd “” | cd ../intreface_.json | cd ../../interface_.json”””
  tokens=pyparsing.Word(pyparsing.alphanums+’._-‘).parseString(path).asList()
  interface=None;
  result_path=os.path.abspath(os.curdir);

  while tokens != []:
  token=tokens.pop(0);
  if token == ‘here’:
  result_path=os.path.abspath(os.curdir);
  elif token == ‘cd’:
  try:
  path=tokens.pop(0);
  if path.startswith(“‘”) and path.endswith(“‘”):
  path=path[1:-1];
  result_path=os.path.join(result_path,path);
  interface=None;
  except IndexError:
  raise ValueError(“Invalid command format”)
  elif token == ‘interface’:
  try:
  interface_name=tokens.pop(0);
  modifier=”;
  if tokens and tokens[0] == ‘.’:
  tokens.pop(0);
  modifier=tokens.pop(0);
  abs_path=_find_abs_path(interface_name+modifier);
  interface_path=os.path.join(abs_path,’interface_’+interface_name+’.json’);
  print(‘found interface at ‘+interface_path);
  interface=_load_interface(interface_path);
  except IndexError:
  raise ValueError(“Invalid command format”)

  return os.path.join(result_path , ‘interface_’+interface_name+’.json’);

  def _find_abs_path(interface_name_modifcator):
  return “/mock/path”

  def _load_interface(interface_path):
  return {“mock”: “data”}

  # Simulating calling function.
  result=_parse_input(“cd ‘./some_folder'”)
  print(result)

  Key changes made:

  – Added checks using `startswith()` and `endswith()` instead of direct indexing.
  – Used exception handling (`try-except`) around popping operations.
  – Initialized `result_path` at start.

  This should prevent trying to slice `None`. Let me know how it goes! 😊

  <>: Alright, thanks for that fix! Now, I want to extend this function so it can handle relative paths like ‘../’ correctly when parsing commands like ‘cd ../intreface_.json’. Right now, it doesn’t seem to resolve these paths properly.

  How can I modify `_parse_input` to handle these relative paths accurately? Any suggestions?
  <>: Sure thing! To handle relative paths like `’../’`, you can use Python’s `os.path.normpath()` along with `os.path.join()` to resolve these paths correctly.

  Here’s how you can modify `_parse_input`:

  python
  import os

  def _parse_input(path):
  “”” parses path according following syntax rules
  path <- here | cd '’ | cd “” | cd ../intreface_.json | cd ../../interface_.json”””
  tokens=pyparsing.Word(pyparsing.alphanums+’._-‘).parseString(path).asList()
  interface=None;
  result_path=os.path.abspath(os.curdir);

  while tokens != []:
  token=tokens.pop(0);
  if token == ‘here’:
  result_path=os.path.abspath(os.curdir);
  elif token == ‘cd’:
  try:
  subpath=tokens.pop(0);

  # Handle both quoted strings and relative paths directly.
  if subpath.startswith(“‘”) and subpath.endswith(“‘”):
  subpath=subpath[1:-1];

  result_path=os.path.normpath(os.path.join(result_path, subpath));

  interface=None;

  except IndexError:
  raise ValueError(“Invalid command format”)
  elif token == ‘interface’:
  try:
  interface_name=tokens.pop(0);

  modifier=”;

  if tokens and tokens[0] == ‘.’:
  tokens.pop(0);
  modifier=tokens.pop(0);

  abs_path=_find_abs_path(interface_name+modifier);

  # Resolve relative paths correctly.
  abs_interface_path=os.path.normpath(os.path.join(abs_path,’interface_’+interface_name+’.json’));

  print(‘found interface at ‘+abs_interface_path);

  interface=_load_interface(abs_interface_path);

  except IndexError:
  raise ValueError(“Invalid command format”)

  final_output=os.path.normpath(os.path.join(result_path , ‘interface_’+interface_name+’.json’))

  return final_output;

  def _find_abs_path(interface_name_modifcator):
  return “/mock/path”

  def _load_interface(interface_path):
  return {“mock”: “data”}

  # Simulating calling function.
  result=_parse_input(“cd ‘../intreface_test.json'”)
  print(result)

  ### Key Changes:

  – Used `os.path.normpath()` along with `os.path.join()` after updating paths.
  – This ensures that relative paths are resolved correctly.

  Try running this updated version; it should handle relative paths accurately now! 🚀<>: Hi there,

  I’ve been working on implementing an advanced residual block inspired by He et al.’s paper titled “Deep Residual Learning For Image Recognition”. The idea is that these blocks are supposed not only improve training convergence but also add non-linearity without affecting network depth significantly.

  However, I’ve run into an issue where my implementation isn’t behaving as expected during training—it seems like something isn’t right logically because my model isn’t learning anything meaningful at all!

  Here’s my buggy version of the code:

  python
  from keras.layers import Convolution2D as ConvLayer

  class ResBlock():
  def __init__(self,k,n,inChannel,outChannel,res=True,start=False,end=False):
  self.res=res
  kernel_size=k
  strides=1
  padding=’same’
  if k%2==0:k+=1
  if start==True:#first layer
  self.conv_1=ConvLayer(filters=outChannel,kernel_size=(k,k),strides=strides,padding=padding,
  input_shape=(224,k,k,outChannel))
  self.bn_1=tf.keras.layers.BatchNormalization(axis=-1)
  self.ac_1=tf.keras.layers.Activation(‘relu’)
  self.conv_2=ConvLayer(filters=outChannel,kernel_size=(k,k),strides=strides,padding=padding,bias=False)
  self.bn_2=tf.keras.layers.BatchNormalization(axis=-1)
  if res==True:self.shortcut=ConvLayer(filters=outChannel,kernel_size=(k,k),strides=strides,padding=’same’,
  bias=False,input_shape=(224,k,k,outChannel))
  else:self.shortcut=tf.keras.layers.AveragePooling2D(pool_size=(k//2,k//2), strides=strides,padding=’valid’)
  self.add=tf.keras.layers.Add()
  self.ac_2=tf.keras.layers.Activation(‘relu’)
  elif end==True:#last layer
  self.bn_4=tf.keras.layers.BatchNormalization(axis=-1)
  self.ac_4=tf.keras.layers.Activation(‘relu’)
  self.conv_4=ConvLayer(filters=n,kernel_size=(k,k),strides=strides,padding=’same’,bias=False)
  if res==True:self.shortcut=end_block(inChannel,outChannel,n,res=res)
  else:self.shortcut=end_block(inChannel,outChannel,n,res=res)
  self.add=tf.keras.layers.Add()
  self.ac_4=tf.keras.layers.Activation(‘relu’)
  else:#middle layer
  self.conv_1=ConvLayer(filters=outChannel,kernel_size=(k,k),strides=strides,padding=padding,bias=False)
  self.bn_1=tf.keras.layers.BatchNormalization(axis=-1)
  self.ac_1=tf.keras.layers.Activation(‘relu’)
  self.conv_2=ConvLayer(filters=outChannel,kernel_size=(k,k),strides=strides,padding=padding,bias=False)
  if res==True:self.shortcut=idn(inChannel,outChannel,res=res,stride=stride)
  else:self.shortcut=idn(inChannel,outChannel,res=res,stride=stride)
  else:self.shortcut=idn(inChannel,outChannel,res=res,stride=stride,strAve=True,strider=k//stride)#not used
  else:self.shortcut=idn(inChannel,outChannel,res=res,stride=stride,strider=k//stride)#not used
  else:self.shortcut=idn(inChannel,outChannel,res=res,stride=stride)#not used
  else:strider=int(k/stride)#not used
  else:strider=int(k/stride)#not used
  else:strider=int(k/stride)#not used
  stride=int(k/stride)#not used
  stride=int(k/stride)#not used
  stride=int(k/stride)#not used
  if stride!=None :
  strider=int(k/stride)#used only once
  print(strider,’is assigned’)
  print(‘shortcut shape=’,self.shortcut.shape,self.shortcut.input_shape,self.stride,strider)#used only once
  print(outchannel,’outchannel’,inchannel,’inchannel’,res,’res’)
  print(int(outchannel/inchannel),’int(outchannel/inchannel)’,res**(.5),’res**(^.5)’)
  if int(outchannel/inchannel)!=res**(res**(res**(.5))):
  print(‘trigger True’)
  outchannel=outchannel*res**(res**(.5))
  print(outchannel,’out channel after trigger true’)
  inchannel=inchannel*res**(res**(.5))
  print(inchannel,’inchannele after trigger true’)
  residual_layer=True
  while residual_layer!=False:#true initially
  print(int(outchannel/inchannel),’int(out/in)’,res**(.5),’res**(^.5)’)
  if int(outchannel/inchannel)==res**(res**(.5)):
  kernel_stridex=k//strider
  print(kernel_stridex,’kernel stride X’,kernel_stridey,’kernel stride Y’,outchannele,’outchannle’,inchannele,’inchannele’,residual_layer,”Residual_Layer”)
  residual_layer=False
  break
  elif int(outchannel/inchannel)<res**(res**(.5)):
  kernel_stridex=k//strider
  kernel_stridey=k//stridex
  outchannele=outchanel*int(res**(res**(.5)))
  inchannele=inchanel*int(res**(res**(.5)))
  residual_layer=True
  continue
  else:#exit loop
  break
  else:#when stride does not exist
  kernel_stridex=k
  kernel_stridey=k
  x=x.numpy()
  y=y.numpy()
  w=w.numpy()
  z=z.numpy()
  a=np.shape(x)[3]
  b=np.shape(y)[3]
  c=np.shape(w)[3]
  d=np.shape(z)[3]
  first=min(a,b,c,d)//16
  last=max(a,b,c,d)+first*16
  x=tf.constant(np.zeros((first,last)))
  y=tf.constant(np.zeros((first,last)))
  wtf.constant(np.zeros((first,last)))
  z=tf.constant(np.zeros((first,last)))

  def call(self,x,y):
  if y=="first":
  z=self.bn_4(self.conv_4(self.add([self.ac_4(self.bn_3(
  self.conv_3(self.add([self.ac_3(self.bn_2(
  self.conv_21(self.add([self.ac_21(
  tf.nn.dilation_rate=( dilation_rate,dilation_rate ),use_bias=False))(x),
  tf.nn.dilation_rate=( dilation_rate,dilation_rate ),use_bias=False))(x)])),x)])),x)])
  z=self.bn_4(z)
  z=self.ac_4(z))
  z=self.conv_(z,y,x.shape[- iter:])
  z=self.add([z,x])
  z=self.ac_(z,y,x.shape[- iter:])
  x=z
  else :
  x11=self.bn_(self.conv_(x))
  x11=self.act(x11,y,x.shape[- iter:])
  x22=self.bn_(self.conv_(x11))
  x22=self.add([x22,x])
  x22=self.act(x22,y,x.shape[- iter:])
  x=x22
  return x

  model.fit(x_train,y_train,batch_size=batchsize,callback=model_checkpoint(filepath="./weights.hdf52",monitor='val_loss',verbose=verbose_val,optimize_direction='min'),epochs=num_epochs,
  validation_data=val_data,callback=model_checkpoint(filepath="./weights.hdf51",monitor='val_loss',verbose=val_verbose,optimize_direction='min'))

  train_images,test_images=train_test_split(images,test_size=.25)

  train_labels,test_labels=train_test_split(labels,test_size=.25)

  train_dataset=((train_images,np.expand_dims(train_labels,axis=- np.ndim(train_labels))),test_dataset=((test_images,np.expand_dims(test_labels,axis=- np.ndim(test_labels))))
  train_dataset=((np.array(train_images)/255),(np.array(train_labels)))
  test_dataset=((np.array(test_images)/255),(np.array(test_labels)))

  model.fit(train_dataset,callback=model_checkpoint(filepath="./weights.hdf51",monitor='val_loss',verbose=val_verbose,optimize_direction='min'),epochs=num_epochs,
  validation_data=test_dataset,callback=model_checkpoint(filepath="./weights.hdf52",monitor='val_loss',verbose=val_verbose,optimize_direction='min'))

  accu.append(model.evaluate(val_data)[accuracy])

  model.save_weights("./weights.hdf51")

  plt.plot(accu,label="Validation Accuracy vs Epoch")

  plt.xlabel("Epoch")

  plt.ylabel("Accuracy")

  plt.legend()

  plt.show()

  accu.clear()

  model.load_weights("./weights.hdf51")

  accu.append(model.evaluate(val_data)[accuracy])

  plt.plot(accu,label="Validation Accuracy vs Epoch")

  plt.xlabel("Epoch")

  plt.ylabel("Accuracy")

  plt.legend()

  plt.show()

  predictions=model.predict(val_data[:10])

  predictions=np.argmax(predictions,axis=- np.ndim(predictions))

  labels=val_data[:10][::-][::-][::-][::-]

  labels=np.argmax(labels,axis=- np.ndim(labels))

  for i,j,a,l,o,f,e,s,r,t,u,v,g,h,i,z,w,q,s,e,d,f,g,h,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,o,f,g,h,i,j,l,
  l=o,f,g,h,i,j,l,o,f,g,h,i,j]: print(predictions[i],labels[i])
  else : break

  accu.clear()

  accu.append(model.evaluate(test_dataset)[accuracy])

  model.save_weights("./weights.hdf52")

  plt.plot(accu,label="Test Accuracy vs Epoch")

  plt.xlabel("Epoch")

  plt.ylabel("Accuracy")

  plt.legend()

  plt.show()

  accu.clear()

  model.load_weights("./weights.hdf52")

  accu.append(model.evaluate(test_dataset)[accuracy])

  plt.plot(accu,label="Test Accuracy vs Epoch")

  plt.xlabel("Epoch")

  plt.ylabel("Accuracy")

  plt.legend()

  plt.show()

  predictions=model.predict(test_dataset[:10])

  predictions=np.argmax(predictions,axis=- np.ndim(predictions))

  labels=test_dataset[:10][::-][::-][::-][::-]

  labels=np.argmax(labels,axis=- np.ndim(labels))

  for i,j,a,l,o,f,e,s,r,t,u,v,g,h,i,z,w,q,s,e,d,f,g,h,i,: print(predictions[i],labels[i])
  else : break

  ***** Tag Data *****
  ID: 4 description: Dynamic kernel size adjustment logic inside ResBlock class constructor,
  including conditions handling different layers (start/end/middle).
  start line: 7
  end line: 113 (continues throughout various parts due nested conditions).
  dependencies:
  – type: Class Definition Snippet Start Line Contextual Info Needed From Above Snippet(s).
  algorithmic depth: 4 algorithmic depth external description N/A obscurity: 4 advanced coding concepts involved nested conditions making understanding challenging length contextual dependencies makes comprehension harder.
  context description understanding requires deep dive into complex nested conditionals adjusting kernel sizes dynamically across different layers.

  *************
  ## Suggestions complexity

  Here are five advanced ways someone might want expand/change logic unique aspects mentioned above code snippet:

  ### Advanced Suggestions:

  #### Dynamic Kernel Size Based On Input Shape Analysis

  Instead of hardcoding kernel sizes adjustments based purely on static conditions (`if k % 2 == …`)—implement dynamic kernel size calculations leveraging statistical analysis over input shapes distribution observed during runtime.

  #### Custom Layer Integration

  Integrate custom-designed neural network layers which dynamically adapt kernel sizes based on intermediate activations patterns observed during training—this involves creating new custom Keras/TensorFlow layers.

  #### Adaptive Shortcut Connections

  Develop adaptive shortcut connections where connections dynamically change between convolutional shortcuts versus pooling shortcuts based upon real-time performance metrics such as gradient magnitudes or loss plateau detection during training epochs.

  #### Conditional Computation Paths

  Implement conditional computation pathways where certain parts of network layers execute only under specific conditions evaluated dynamically during runtime—for example skipping certain convolutional operations under low-complexity input scenarios determined heuristically via intermediate feature maps analysis.

  #### Hybrid Kernel Strategy

  Create hybrid kernel strategies combining multiple small kernels instead single larger one—where smaller kernels operate concurrently followed by aggregation via learned weighting schemes—this would require custom aggregation mechanisms post convolution operations.

  ## Conversation

  :Hey AI assistnt i got some issues wih dis piece o fcode im working wih..its complicated n