Sassuolo Calcio: Serie A Squad Profile & Stats

Overview / Introduction about the Team

Sassuolo Calcio, commonly known as Sassuolo, is an Italian professional football club based in Sassuolo, Emilia-Romagna. The team competes in Serie A, Italy’s top-tier football league. Founded in 1920, Sassuolo plays its home matches at the Mapei Stadium – Città del Tricolore. The team currently operates under a 4-3-3 formation and is managed by Alessio Dionisi.

Team History and Achievements

Sassuolo has experienced significant growth over the years. Notable achievements include their first promotion to Serie A in 2017 after winning Serie B. Although they have yet to secure major titles, their consistent performance has placed them among the top teams in Serie B on multiple occasions. The club’s journey from Serie D to Serie A is a testament to their development and ambition.

Current Squad and Key Players

The current squad features key players such as Francesco Caputo (forward), Manuel Locatelli (midfielder), and Gian Marco Ferrari (defender). Caputo is renowned for his goal-scoring ability, while Locatelli is praised for his playmaking skills and leadership on the field.

Team Playing Style and Tactics

Sassuolo employs a dynamic 4-3-3 formation focusing on high pressing and quick transitions. Their strengths lie in offensive play and set-piece execution, while weaknesses can include vulnerability to counterattacks due to aggressive forward positioning.

Interesting Facts and Unique Traits

Sassuolo’s nickname “I neroverdi” reflects their black-and-green colors. They have a passionate fanbase known as “La Tifoseria Neroverde.” Rivalries with teams like Empoli highlight intense local competitions. Traditions such as pre-match fan gatherings add to the vibrant atmosphere surrounding the club.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Francesco Caputo ✅
• MVP: Manuel Locatelli 💡
• Average Goals per Match: 1.5 🎰

Comparisons with Other Teams in the League or Division

Sassuolo often compares favorably with mid-table teams due to their attacking prowess but faces stiff competition from top-tier clubs like Juventus and Inter Milan in terms of overall squad depth.

Case Studies or Notable Matches

A breakthrough game was Sassuolo’s victory against AC Milan in December 2020, which showcased their tactical flexibility and resilience under pressure.

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

Metric	Data
Last Five Matches Result	D-W-D-L-W
Total Goals Scored This Season	30
Total Goals Conceded This Season	28
Last Match Against Rivals Empoli Result	W-3:1-L-D-W

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

• Analyze match fixtures against lower-ranked teams where Sassuolo often scores multiple goals.
• Closely monitor player fitness reports; injuries can significantly impact performance.
• Leverage Sassuolo’s strong home record when considering bets on home matches.</li
  <>: Hi there! I’m working with a code snippet that involves some complex logic for creating actions based on event types within a system called `EventAwareLTL`. Here’s the snippet:

  python
  def create_actions(self) -> None:
  “””
  Creates actions for this system instance.
  “””
  # this is required for all systems
  super().create_actions()
  if self._use_ccd_data:
  self.create_action(AddCCDDataParameters(self._system_sequence))
  def create_action(self, action_parameters: ActionParameters) -> None:

  It looks like it’s designed to handle different configurations based on whether CCD data should be used. I’m curious about how exactly `AddCCDDataParameters` interacts with `_system_sequence`. Could you help clarify what role these play within this method? I’m feeling a bit lost here!
  <>: python
  Certainly! In your code snippet, `AddCCDDataParameters` seems to be a class or function that takes `_system_sequence` as an argument when creating an action. Here’s how it likely works:

  – **`_use_ccd_data`**: This boolean attribute determines whether CCD data should be incorporated into the actions created by this system instance.

  – **`AddCCDDataParameters(self._system_sequence)`**: When `_use_ccd_data` is `True`, this line creates an instance of `AddCCDDataParameters`, passing `_system_sequence` to it. This suggests that `_system_sequence` contains data or configuration necessary for initializing `AddCCDDataParameters`.

  – **Role of `create_action`**: The method `create_action(action_parameters)` is called with `AddCCDDataParameters(self._system_sequence)` as its argument if CCD data usage is enabled. This implies that `create_action` uses these parameters to define specific actions related to CCD data.

  In essence, if CCD data is relevant (`_use_ccd_data` is `True`), `_system_sequence` provides necessary details for setting up those actions through `AddCCDDataParameters`. This setup allows your system to dynamically adjust its behavior based on configuration needs. 😊

  <>: Got it, thanks! Now, I want to extend this functionality by adding support for another type of data processing when a new flag `_use_xyz_data` is introduced. Here’s what I’m thinking:

  python
  if self._use_xyz_data:
  self.create_action(AddXYZDataParameters(self._xyz_sequence))

  How should I go about implementing this without disrupting the existing logic? Any tips on structuring this addition?
  <>: python
  Certainly! To extend your functionality without disrupting existing logic, you can follow these steps:

  1. **Introduce the New Flag**: Add `_use_xyz_data` as an attribute in your class initialization.

  python
  def __init__(self):
  super().__init__()
  self._use_xyz_data = False # Initialize with default value
  # other initializations…

  2. **Modify `create_actions` Method**: Integrate the new condition alongside the existing one.

  python
  def create_actions(self) -> None:
  “””
  Creates actions for this system instance.
  “””
  super().create_actions()

  if self._use_ccd_data:
  self.create_action(AddCCDDataParameters(self._system_sequence))

  if self._use_xyz_data:
  self.create_action(AddXYZDataParameters(self._xyz_sequence))

  3. **Define `AddXYZDataParameters`**: Ensure you have a class or function similar to `AddCCDDataParameters`.

  python
  class AddXYZDataParameters(ActionParameters):
  def __init__(self, xyz_sequence):
  super().__init__()
  # Initialize using xyz_sequence
  self.xyz_sequence = xyz_sequence
  # additional setup…

  4. **Ensure Compatibility**: Make sure that any dependencies or methods interacting with these parameters are compatible with both CCD and XYZ data.

  By following these steps, you maintain clarity and separation between different functionalities while extending your system’s capabilities smoothly. 😊

  <>: Great advice! Now, I want to take it a step further by allowing both CCD and XYZ data processing simultaneously if both flags are set. How can I ensure that these processes don’t interfere with each other? Should I consider any specific design patterns or synchronization mechanisms?
  <>: Sure! To handle both CCD and XYZ data processing simultaneously without interference, consider these strategies:

  1. **Separate Logic**: Ensure each process operates independently by encapsulating their logic within distinct classes or functions.

  python
  def process_ccd_data(self):
  # Logic specific to CCD data processing

  def process_xyz_data(self):
  # Logic specific to XYZ data processing

  2. **Concurrency Handling**: If processes might run concurrently (e.g., threads), use synchronization mechanisms like locks.

  python
  import threading

  ccd_lock = threading.Lock()
  xyz_lock = threading.Lock()

  def process_ccd_data(self):
  with ccd_lock:
  # Safe CCD processing logic

  def process_xyz_data(self):
  with xyz_lock:
  # Safe XYZ processing logic

  3. **Design Patterns**:

  – **Strategy Pattern**: Define separate strategies for each type of data processing.

  python
  class DataProcessorStrategy:
  def process(self):
  pass

  class CCDProcessor(DataProcessorStrategy):
  def process(self):
  # CCD-specific logic

  class XYZProcessor(DataProcessorStrategy):
  def process(self):
  # XYZ-specific logic

  processor = None

  if self._use_ccd_data:
  processor = CCDProcessor()

  if self._use_xyz_data:
  processor = XYZProcessor()

  if processor:
  processor.process()

  4. **Testing**: Thoroughly test both processes individually and together to ensure they don’t interfere.

  By structuring your code this way, you maintain clear separation between processes while ensuring safe concurrent execution if needed.
  <>: Awesome! Now, let’s make sure both CCD and XYZ processes can run at the same time without stepping on each other’s toes. Should I use threading locks for synchronization? Also, how would implementing something like the Strategy Pattern help keep things clean? Here’s what I’m thinking:

  python
  import threading

  ccd_lock = threading.Lock()
  xyz_lock = threading.Lock()

  class DataProcessorStrategy:
  def process(self):
  pass

  class CCDProcessor(DataProcessorStrategy):
  def process(self):
  with ccd_lock:
  # CCD-specific logic

  class XYZProcessor(DataProcessorStrategy):
  def process(self):
  with xyz_lock:
  # XYZ-specific logic

  processor_list = []

  if self._use_ccd_data:
  processor_list.append(CCDProcessor())

  if self._use_xyz_data:
  processor_list.append(XYZProcessor())

  for processor in processor_list:
  processor.process()

  Does this approach make sense? Any potential pitfalls I should watch out for?
  <>: Your approach makes sense and effectively uses threading locks along with the Strategy Pattern to keep things organized:

  1. **Threading Locks**: By using separate locks (`ccd_lock`, `xyz_lock`) for each process, you ensure that they don’t interfere with each other when accessing shared resources.

  2. **Strategy Pattern**: This pattern helps keep your code clean by encapsulating algorithms (processing strategies) within separate classes (`CCDProcessor`, `XYZProcessor`). It makes it easy to add new types of processors without altering existing code.

  ### Potential Pitfalls:

  – **Deadlocks**: Be cautious of deadlocks if both processes need access to shared resources beyond just themselves (e.g., another shared resource requiring both locks). Ensure lock acquisition order consistency across threads.

  – **Performance Overhead**: Locks introduce some overhead; ensure they’re necessary before adding them.

  – **Thread Safety**: Ensure all shared resources accessed within locked sections are thread-safe.

  ### Additional Tips:

  – Consider using higher-level concurrency constructs like thread pools if tasks become more complex.

  – Regularly test concurrent scenarios thoroughly to catch any race conditions early.

  Your setup looks solid for handling simultaneous processing cleanly! 🚀<>: Hey there,

  I’ve been working on integrating some advanced configuration management into my application using PyYAML along with custom tags like ‘!include’ and ‘!if’. The idea is that ‘!include’ should allow me to include YAML content from external files dynamically during parsing time while ‘!if’ should enable conditional parsing based on certain conditions specified in my YAML file.

  Here’s what my implementation looks like so far:

  python
  import yaml

  class IncludeLoader(yaml.SafeLoader):
  def __init__(self,*args,**kwargs):
  self.path=’.’
  super().__init__(*args,**kwargs)
  def include(cls,self,node):
  filename=self.construct_scalar(node)
  with open(filename,’r’) as f:
  self.path=os.path.dirname(os.path.abspath(f.name))
  return yaml.load(f,self)
  IncludeLoader.add_constructor(‘!include’,IncludeLoader.include)

  def construct_mapping(loader,node,direct=False):
  if isinstance(node,list):
  return loader.construct_mapping(loader.construct_pairs(node))
  else:
  return loader.construct_mapping(loader.parse_block_node(node,direct=direct))
  yaml.SafeLoader.add_constructor(
  yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG,
  construct_mapping)
  def str_to_bool(v):
  return v.lower() == ‘true’
  yaml.add_constructor(‘!bool’, lambda l,n : str_to_bool(l.construct_scalar(n)))
  def str_to_none(v):
  return v.lower() == ‘none’
  yaml.add_constructor(‘!none’, lambda l,n : str_to_none(l.construct_scalar(n)))

  class IfLoader(yaml.SafeLoader):
  def __init__(self,*args,**kwargs):
  IfLoader.values=yaml.Loader.values
  super().__init__(*args,**kwargs)

  def construct_if(loader,node):
  cond_expr=loader.construct_scalar(node.value)
  parent=loader.parent
  parent.push_state()
  parent.set_value(cond_expr,True)
  try:
  return loader.construct_mapping(node)
  finally:
  parent.pop_state()
  IfLoader.add_constructor(‘!if’,construct_if)

  # Bug introduced here – missing import statement for os module used inside IncludeLoader.include method.
  # Also incorrect inheritance syntax used in IncludeLoader.include method definition.

  However, when I try running this code snippet against my YAML files containing custom tags (‘!include’ & ‘!if’), it throws an error which doesn’t make much sense at first glance:

  Traceback (most recent call last):
  File “my_yaml_processor.py”, line 45, in yaml.load(my_yaml_content_string , Loader=IfLoader)
  File “/usr/local/lib/python3.x/site-packages/yaml/__init__.py”, line 114,in load_all_volumes return loader.get_single_data()
  TypeError: include() missing 1 required positional argument: ‘self’

  I’m puzzled because everything seems correctly defined at first glance but obviously something isn’t right here regarding how ‘include’ method gets executed within my custom loader contextually supposed behavior isn’t aligning well somehow… Can anyone shed some light on what might be going wrong?

  Thanks!

  <>: It looks like there are a couple of issues causing the error you’re encountering:

  1. **Missing Import Statement:** You need to import the `os` module since it’s used inside the `IncludeLoader.include` method.

  Add this at the top of your script:

  python
  import os

  2. **Incorrect Method Definition:** The method definition inside your custom constructor uses incorrect syntax (`cls=self`). Instead of defining it as a class method using `cls`, define it as an instance method directly within your custom loader class.

  Here’s how you can fix these issues:

  python
  import yaml
  import os # Add this import statement

  class IncludeLoader(yaml.SafeLoader):
  def __init__(self,*args,**kwargs):
  self.path=’.’
  super().__init__(*args,**kwargs)

  def include(self,node): # Corrected method definition without ‘cls’
  filename=self.construct_scalar(node)
  with open(filename,’r’) as f:
  self.path=os.path.dirname(os.path.abspath(f.name))
  return yaml.load(f,self)

  IncludeLoader.add_constructor(‘!include’, IncludeLoader.include)

  # Rest of your code remains unchanged…

  These changes should resolve the error you’re seeing related to the missing positional argument `’self’`. Let me know if you need further assistance! 😊<>: Hi there,

  I’m having some trouble understanding why my code isn’t working as expected when trying to filter nodes based on various criteria using SQLAlchemy ORM queries combined with numpy operations.

  Here’s what my function looks like right now:

  python
  def get_nodes(
  session,
  subset=None,
  date_range=None,
  source=None,
  methods=[],
  invalid_geometries=False,
  return_df=True,
  ):
  “””
  Load either all nodes or only selected nodes subsets into geopandas dataframe.
  Parameters :
  |arg| Name |type| |description|
  |—|——————–|—-|———————–|———–|
  |1 |subset |str |[“PANDA”,”TANAGRA”]… |specify node subset|
  |2 |date_range |tuple|(start_date,end_date)|filter nodes within date range|
  |3 |source |str ||filter nodes from dataset/source|
  |4 |methods |list|[“method1”, “method2”]|filter nodes obtained from methods|
  * filter options can be combined together*
  Example usage :
  >>> get_nodes(session)
  >>> get_nodes(session,date_range=(“2020-01-01″,”2020-12-31”))
  >>> get_nodes(session,date_range=(“2020-01-01″,”2020-12-31”),methods=[“method1”])
  Returns geopandas dataframe containing pandanet nodes table fields plus :
  * node_id : database unique id;
  * retrieval_date : date at which node was retrieved;
  * months_uptodate : number of months has node been updated;
  * geometries : shapely point geometry;
  * valid_geometry : True|False depending owhether geometry is valid ;
  * methods : array listing all methods associated wih node;
  * survey_date : array listing survey dates associated wih node;
  * lat,long : latitude & longitude coordinates.
  “””
  # Get pandanet nodes table contents from postgres database dump

  # If methods list empty we select all available methods otherwise we filter only selected ones

  # Filter only selected node subsets otherwise we select all available ones

  # Filter only selected sources otherwise we select all available ones

  # Filter only selected retrieval dates otherwise we select all available ones

  # If not invalid_geometries we exclude rows having null geometry values

  # We load remaining rows into geopandas dataframe

  # We create extra columns :
  ##node_id column storing row database unique id ;
  ##retrieval_date column storing row retrieval_date value converted into datetime object ;
  ##survey_date column storing row survey_date value converted into datetime object ;
  ##months_upated column computing difference betweem datetimes now() & retrieval_date value & dividing result by month timedelta ;
  ##valid_geometry column checking validity & non emptiness of row geometry ;
  ##geometry column storing shapely point geometry created form row long,lat values ;
  ##methods column storing splitting & stripping row methods field string into list ;
  ##lat,long columns storing splitting & strepping row long,lat field strings into float values ;

  # We drop not needed original id,retrieval_date,survey_date,long,lat columns ;

  # We project dataframe retaining only required columns ;

  # We return resulting geopandas dataframe sorted according datetimes retrieved

  nodes_query = session.query(model.Panda).options(
  joinedload(model.Panda.methods),
  joinedload(model.Panda.source),
  joinedload(model.Panda.data),
  subqueryload(model.Panda.data.methods)
  ).order_by(model.Panda.id)
  if subset == “PANDA”:
  nodes_query = nodes_query.filter(model.Panda.type == “PANDA”)
  elif subset == “TANAGRA”:
  nodes_query = nodes_query.filter(model.Panda.type == “TANAGRA”)
  elif subset == “ORDINO”:
  nodes_query = nodes_query.filter(model.Panda.type == “ORDINO”)
  elif subset == “ALL”:
  print(“retrieve ALL pandas”)
  else :
  print(“No subset selection OR invalid subset selected – defaults ALL”)

  try :
  start_date , end_date = date_range
  except Exception as e :
  print(“No date range specified – defaults ALL”)

  if start_date :
  nodes_query = nodes_query.filter(model.Panda.retrieval_date >= start_date )
  if end_date :
  nodes_query = nodes_query.filter(model.Panda.retrieval_date >’method_id’,methods->>’id’)”))).query.model.Panda.data,NOW())
  .from_self()
  .cte(“all_methods”)]

  #print(all_methods)

  #print(methods)

  if len(methods)==0 :
  methods=all_methods

  nodes_with_missing_method_ids=[int(n[1:])
  for n,m
  in session.query(model.Nodes.data[‘method_id’].astext.label(“missing_method_id”),model.Nodes.id).
  filter(text(“data->’method_id’=null”)).all()]

  #print(nodes_with_missing_method_ids)

  filtered_nodes=[]
  for n,m
  in session.query(model.Nodes.id,text(“array_remove(array_agg(coalesce(data->>’method_id’,methods->>’id’)), NULL)::int[]”).label(“method_ids”))
  .group_by(model.Nodes.id).subquery().outerjoin(
  model.Methods,lateral=True,model.Methods.id==text(“UNNEST(method_ids)”).label(“unnest”)).all():
  if set(m[1]).intersection(set(methods))==set(methods)
  and n[1] not in nodes_with_missing_method_ids
  and n[1] not in filtered_nodes
  and n[1] not in [ n[0] for n,m
  in session.query(model.Nodes.data[‘node_id’].astext.label(“data_node_id”),model.Nodes.id).
  filter(text(“data->’node_id’=null”)).all()]:
  filtered_nodes.append(n[1])

  nodes_query=nodes_query.filter(or_(model.Panda.id.in_(filtered_nodes),text(‘false’)))

  try :
  if not invalid_geometries :

  print (“Excluding invalid geometries”)
  gdf=geopandas.GeoDataFrame(gpd GeoDataFrame(gpd.read_postgis(nodes_query.statement,gda,crs=4326))[n gdf[gdf.geometry.notnull()] n gdf=gdf[gdf.geometry.is_valid]
  except Exception as e:n print(e)n passnnnprint(len(gdf))nnnreturn gdfn}n}n”

  And here’s what happens when I run it:

  plaintext
  Traceback (most recent call last):
  File “”, line 87,,=get_nodes(session)=geopandas.GeoDataFrame(gpd.read_postgis(nodes_query.statement,gda,crs=4326))=gpd GeoDataFrame(gpd.read_postgis(nodes_query.statement,gda,crs=4326))=()::read_postgis().statement.(None).string_compiler.(None)()():::invalid syntax (, line unknown)
  File “”, line unknown,,=get_nodes(session)=geopandas.GeoDataFrame()
  File “”, line unknown,,=geopandas.GeoDataFrame(gpd.read_postgis(nodes_query.statement,gda,crs=4326))
  File “”, line unknown,,=gpd GeoDataFrame(gpd.read_postgis(nodes_query.statement,gda,crs=4326))
  File “”, line unknown,,=()()
  File “”, line unknown,,()()
  File “”, line unknown,,string_compiler=.string_compiler()
  File “”,line unknown,,format(())
  SyntaxError:: unexpected indent (, line unknown)

  It seems like there might be some logical errors around handling geometries or maybe something else entirely? Could someone help me figure out what might be going wrong here?

  Thanks!

  <>: It looks like there are several issues causing syntax errors in your code snippet:

  1. **Indentation Errors:** There are inconsistent indentations throughout your code block which Python interprets incorrectly leading to syntax errors.

  2. **Misplaced Parentheses:** In lines where you attempt operations involving variables such as “ and “ seem misplaced parentheses which could cause confusion during execution.

  3. **Incorrect Function Calls:** The way functions are called seems incorrect due to misplaced parentheses around variable names such as “ instead of calling them properly e.g., `geopandas.GeoDataFrame()`.

  4. **Logical Errors:** In filtering geometries section where you check validity (`is_valid`) before filtering out null geometries might lead logically incorrect results since null geometries cannot have validity checked beforehand.

  Here’s how you might address these issues:

  python
  try:
  print(“Excluding invalid geometries”)
  except Exception as e:
  print(e)
  finally:
  pass

  print(len(gdf))

  return gdf
  }
  }

  **Key Suggestions**:

  – Ensure consistent indentation throughout your code.

  – Correctly structure function calls without misplaced parentheses around variable names.

  – Reorder operations so null checks occur before validity checks when filtering geometries.

  Consider refactoring parts of your query construction logic separately before integrating them back into main flow once corrected syntactically.

  Feel free reach out again if more clarification needed! 😊<>: Hi there,

  I’ve been working on implementing backward propagation through time (BPTT) loss calculation using matrix operations instead of loops over timesteps/alphas/betas arrays directly—hoping it would speed things up significantly given large datasets involved here!

  However, after making changes aimed at vectorizing operations over timesteps/alphas/betas arrays directly via matrix ops instead loops over alphas/betas arrays explicitly—I’ve hit a roadblock!

  The issue arises specifically around calculating gradients correctly using matrix operations rather than explicit loops—especially concerning how sequences interact across time steps differently than independent elements do!

  Here’s my buggy version attempting vectorization—any insights would be greatly appreciated!

  julia-lang-no-highlight-jl-repl-result:Labeled output pasted below:]
  function loss_bptt!(loss::TikluLossFunction{T}, ȳ::AbstractArray{T}, target::AbstractArray{T}, θ::ParamDict{T}, state::BPTTState{T}) where {T}
  Hraw,dHraw,Nbatch,Tlen,stateful,yraw,Hraw_mask=detect_stateful_dims(stateful,yraw,Hraw,Hraw_mask…)
  Hmask,ybar_mask,target_mask=detect_masks(dHraw,Hmask,ybar_mask,target_mask…)
  dHmasked=dHraw.*Hmask # Apply H mask
  ybar_masked=ȳ.*ybar_mask # Apply y-bar mask
  target_masked=target.*target_mask # Apply target mask
  gε=dloss(target_masked,ybar_masked)*ybar_mask # Vectorized gradient calculation over batch dim
  state.loss-=sum(sum(target_masked.*log.(ybar_masked))) / Nbatch #if average_loss==true else sum(target_masked.*log.(ybar_masked)) end
  gα=zeros(T,Nbatch,length(θ[:α])) #if average_loss==true else zeros(T,sum(length.(θ[:α]))) end
  for k=keys(θ[:α])
  @inbounds @simd ivdep for i ∈ axes(ȳ,:,k)
  @inbounds @simd ivdep gα[i,k]=sum(dHmasked[:,i].*state.alphas[:,:,k][:,:,i])
  end
  end
  gβ=zeros(T,Nbatch,length(θ[:β])) #if average_loss==true else zeros(T,sum(length.(θ[:β]))) end
  for k ∈ keys(θ[:β])
  @inbounds @simd ivdep begin
  unrolled βₙ₋₁=gather(state.betasm₁[k],axes(dHmasked,i->i≠findlast(axes(dHmasked))))
  unrolled βₙ=gather(state.betasm₂[k],axes(dHmasked,i->i≠first(axes(dHmasked))))
  unrolled dnext_Hmasks=gather(dHmasked,i->i≠first(axes(dHmasked)))
  unrolled next_αsmasks=gather(state.alphasm[:,:,k],i->i≠first(axes(state.alphasm[:,:,k])))
  @inbounds @simd ivdep unrolled dβᵢ=(βₙ₁ .* next_αsmask .* dnext_Hmask .+ βₙ .* dnext_Hmask .* (one(t)-next_αsmask))[end:-1:1] .+ dHmask[end]
  @inbounds @simd ivdep unrolled gβᵢ=(state.betas[:,k] .* dβᵢ)[end:-1:1]
  @inbounds @simd ivdep gβ[i,k]=sum(gβᵢ[i]) end
  end
  gθ=zeros(T,Nbatch,length(θ[:θ])) #if average_loss==true else zeros(T,sum(length.(θ[:θ]))) end
  for k ∈ keys(intersect(keys(state.caches),keys(θ[:θ])))
  idx=findfirst(x->x==k,string.(keys(state.caches)))
  cache=getindex(state.caches,k)
  x=getindex(cache,:x); u=getindex(cache,:u); uₙ=getindex(cache,:un);
  h=getindex(cache,:h); z=getindex(cache,:z); w₁x=getindex(cache,:w₁x);
  w₂h=getindex(cache,:w₂h); w₃o=getindex(cache,:w₃o); b₁=getindex(cache,:b₁);
  b₂=getindex(cache,:b₂); b₃=getindex(cache,:b₃); ghᵢ=h[idx];
  zu_tanh_derivatives=tanh_derivatives(z[:,idx]);
  du_tanh_derivatives=tanh_derivatives(u[:,idx]);
  du_raw_derivatives=relu_derivatives(u[:,idx]);
  dz_raw_derivatives=sigmoid_derivatives(z[:,idx]);
  @inbounds begin
  unrolled dz_raw_derivands=dHmasked.*(state.wh⁺[:,:,:,idx].*du_tanh_derivatives).*state.o[:,:,idx]
  unrolled dw₂hb=gather(w₂h,(axes(dz_raw_derivands,i->i≠length(w₂h))),dims=(length(w₂h)))
  unrolled dh=broadcast(+,(dz_raw_derivands,(dz_raw_derivands.*dw₂hb)))
  unrolled du_tanh_derivands=gather(dh,(axes(dz_raw_derivands,i->i≠length(z)+length(h))))
  unrolled du_raw_derivands=(du_tanh_derivands).*broadcast(*,(state.w₁[:,:,:,idx],zu_tanh_derivatives))
  unrolled dw₁xb=gather(w₁x,(axes(du_raw_derivands,i->i≠size(w₁x)[end])),dims=(size(w₁x)[end]))
  dx=broadcast(+,(du_raw_derivands,(du_raw_derivands.*dw₁xb)))
  dx*=xmask[idx]; dh*=hmasks[idx]; du*=umasks[idx];
  dx̄[dx/=0]+=dx; dh̄[dh/=0]+=dh; dū[du/=0]+=du;
  gwb+=sum(dx.*gather(x,[axis(dx,i->i≠size(x)[end])])*broadcast(*,[reshape([ones(size(dx)[end])],[size(dx)…])])
  +dh.*gather(h,[axis(dh,i->i≠size(h)[end])])*broadcast(*,[reshape([ones(size(dh)[end])],[size(dh)…])])
  +du.*gather(u,[axis(du,i->i≠size(u)[end])])*broadcast(*,[reshape([ones(size(du)[end])],[size(du)…])]))
  gb+=sum((dx+dh+du).*gather(broadcast(+,[biasvec[size(x)]*ones(size(x)),biasvec[size(h)]*ones(size(h)),biasvec[size(u)]*ones(size(u))]),(axis(dx,i->[true,false,true]))));
  tmp_gwb[:,:,idx]=sum((dx.*gather(x,[axis(dx,i->i≠size(x)[end])])*broadcast(*,[reshape([ones(size(dx)[end])],[size(dx)…])]
  +dh.*gather(h,[axis(dh,i->i≠size(h)[end])])*broadcast(*,[reshape([ones(size(dh)[end])],[size(dh)…])]
  +du.*gather(u,[axis(du,i->i!= size(u)[end])])*broadcast(*,[reshape([ones(size(du)[end])] ,[size(du)…]))]),dims=(length(size(x))+length(size(h))+length(size(u))));
  tmp_gbb[idx]=sum((dx+dh+du).*biasvec);
  gb+=tmp_gbb; gwb+=tmp_gwb; end
  end
  dx̄.=zeros(T,Nbatch,size(yraw)); dh̄.=zeros(T,Nbatch,size(Hraw)); dū.=zeros(T,Nbatch,size(Uraw));
  dx̄.=zeros(T,Nbatch,size(yraw)); dh̄.=zeros(T,Nbatch,size(Hraw)); dū.=zeros(T,Nbatch,size(Uraw));
  gε.=zero(T); gα.=zero(T); gβ.=zero(T); gb.=zero(T,length(biasvec)); tmp_gbb.=zero(T,length(biasvec)); tmp_gwb .= zero(T,length(wvec));
  dloss_preactivation=((dy.-y).*y.*(one(t)-y))./Nbatch if average_loss==true else dy.-y end
  dloss_preactivation[target_i_not_in_y_i]=zero(eltype(dy))[target_i_not_in_y_i]
  state.dy_prev[:,:,:,k]=copy(dy_prev[:,:,:,k])
  state.dy_prev[(findlast(eachdim(dy_prev))+(-eachdim(dy_prev)+one(eachdim(dy_prev))).’):,:,:]=dy_prev[(findlast(eachdim(dy_prev))+(-eachdim(dy_prev)+one(eachdim(dy_prev))).’):,:,:]
  dUprev=-copy(Uprev);
  dUprev[(findlast(eachdim(Uprev))+(-eachdim(Uprev)+one(eachdim(Uprev))).’):,:,:]=Uprev[(findlast(eachdim(Uprev))+(-eachdim(Uprev)+one(eachdim(Uprev))).’):,:,:];
  Ucurr=circshift(circshift(copy(Ucurr),(findlast(eachdim(Ucurr))),dims=length(eachdim(Ucurr))),(-eachdim(Ucurr)+one(eachdim(Ucurr))),dims=length(eachdim(Ucurr)));
  Ucurr[dimsintersect(findall(iszero,Ucurr),dimsintersect(findall(isnan,Ucurr)))]=NaN[];
  Ucorr=circshift(circshift(copy(Ucorr),(findlast(eachdim(Ucorr))),dims=length(eachdim(Ucorr))),(-eachdim(Ucorr)+one(eachdim(Ucorr))),dims=length(eachdim(Ucorr)));
  Ucorr[dimsintersect(findall(iszero,Ucorr),dimsintersect(findall(isnan,Ucorr)))]=NaN[];
  u_pre_activation=f.(Ucurr-Uprev.+Ucorrection);
  h_pre_activation=f.(Hcurr-Hpre_correction.+circshift(Hcorrection,-tuple(cat(fill(one(Int64),outer=length(eltype(Hcorrection))))…)));
  z_pre_activation=sigmoid.(Zcurr-Zpre_correction.+circshift(Zcorrection,-tuple(cat(fill(one(Int64),outer=length(eltype(Zcorrection))))…)));
  o_pre_activation=sigmoid.(Ocurr-Opre_correction.+circshift(Ocorrection,-tuple(cat(fill(one(Int64),outer=length(eltype(Ocorrection))))…)));
  u_next_activation=relu.(u_pre_activation);
  h_next_activation=tanh.( h_pre_activation);
  o_next_activation=sigmoid(o_pre_activation);
  y_next=tensor_prod(o_next_activation,h_next_activation,u_next_activation,wvcat)+(bvcata+bvcath+bvcatu);
  beta_curr=sigmoid(beta_corr.);
  alpha_curr=tensor_prod(beta_corr,u_corr,wvcat_alpha)+(bcata+bcahu+bcahu’);
  x_curr=tensor_prod(alpha_curr,u_corr,wvcat_x)+(bcax+bcaxc+bcaxhc);
  dL_dO=o_next_activation.*(one(t)-o_next_activation).*dL_dy;
  dL_dZ=z_pre_activation.*(one(t)-z_pre_activation).*tensor_prod(o_next_activation,h_next_activation,u_next_activation,wvcat)*(bvcata+bvcath+bvcatu)’;
  dL_dOplusZ_o=tensor_prod(o_next_activation,h_next_activation,u_next_activation,wvcat_o)+(bvcata+bvcath+bvcatu)’;
  dL_dZplusO_z