Overview of Football EURO U19 Qualification Group 11
The Football EURO U19 Qualification Group 11 is a crucial stage in the journey towards representing Europe at the highest level of youth football. This group features some of the most promising young talents from across the continent, competing fiercely to secure their place in the finals. With fresh matches updated daily, fans and experts alike are keenly following each game, analyzing performances, and making betting predictions.
Understanding the Format
The qualification process is divided into several groups, with Group 11 being one of them. Each team plays against others in their group both home and away. The top teams from each group advance to the next round, while those at the bottom face relegation or have to compete in additional qualifying matches.
Key Teams to Watch
- Team A: Known for their strong defense and tactical play, Team A has consistently performed well in past competitions.
- Team B: With a roster full of attacking talent, Team B is expected to score heavily throughout the qualification rounds.
- Team C: A dark horse in this group, Team C has shown remarkable improvement and could surprise many with their performance.
Daily Match Updates
As matches unfold each day, fans can expect detailed updates on scores, standout players, and key moments that could influence betting odds. These updates are crucial for anyone looking to make informed betting decisions.
Betting Predictions and Analysis
Betting predictions are based on a variety of factors including team form, head-to-head records, player injuries, and even weather conditions. Experts use statistical models and historical data to provide insights that can guide bettors towards more strategic wagers.
Factors Influencing Betting Predictions
- Team Form: Recent performances can indicate how well a team might perform in upcoming matches.
- Head-to-Head Records: Historical matchups between teams can provide insights into potential outcomes.
- Injuries: The absence of key players can significantly impact a team's chances.
- Tactical Changes: Adjustments made by coaches can alter the dynamics of a game.
In-Depth Match Analysis
Each match is analyzed thoroughly by experts who break down strategies employed by both teams. This analysis includes discussions on formations used, pressing intensity, set-piece tactics, and individual player performances. Such detailed breakdowns help fans understand the nuances of each game beyond just the final score.
Tactical Breakdowns
- Formation Analysis: Understanding whether a team uses a 4-3-3 or a 4-4-2 formation can reveal much about their strategic approach.
- Possession Play: Teams that focus on maintaining possession may have different strengths compared to those that rely on counter-attacks.
- Midfield Battles: The midfield often dictates the flow of a game; analyzing these battles can predict which team will control possession and territory.
Prediction Models
Prediction models use algorithms to simulate thousands of potential outcomes based on current data. These models consider variables such as player statistics, historical performance metrics, and even psychological factors like team morale. By running simulations, experts can offer probabilistic predictions about match results.
Data Sources for Prediction Models
- Sports Analytics Platforms: These platforms provide comprehensive datasets that include player stats, match histories, and more.
- Football Databases: Specialized databases offer detailed records of past games which are invaluable for trend analysis.
- Social Media Sentiment Analysis: Analyzing social media trends can give insights into public opinion and potential underdog support.
Betting Strategies for Enthusiasts
Betting enthusiasts should consider diversifying their strategies rather than relying solely on match predictions. This could involve placing bets on various outcomes such as total goals scored or specific player performances like 'first goal scorer' or 'most assists.'
Diversified Betting Options
- Total Goals Over/Under:This bet involves predicting whether the total number of goals scored will be over or under a specified amount set by bookmakers.
- Bet Builder Bets:Create custom bets using multiple selections within one event; this allows for more tailored betting strategies based on personal insights into specific aspects of upcoming games.
None:
[20]: super().__init__()
[21]: self.num_layers = num_layers
if norm_layer is not None:
layer_norms = [
norm_layer(hidden_dim) if i > 0 else nn.Identity()
for i in range(num_layers)
]
self.layer_norms = nn.ModuleList(layer_norms)
else:
self.layer_norms = None
layers = []
if self.num_layers == 1:
layers.append(nn.Linear(input_dim ,hidden_dim))
if act_layer is not None:
layers.append(act_layer())
if dropout > 0:
layers.append(nn.Dropout(dropout))
layers.append(nn.Linear(hidden_dim ,output_dim))
else:
layers.append(nn.Linear(input_dim ,hidden_dim))
if norm_layer is not None:
layers.append(self.layer_norms.pop(0))
if act_layer is not None:
layers.append(act_layer())
if dropout > 0:
layers.append(nn.Dropout(dropout))
for _ in range(num_layers - 1):
layers.extend([
])
def forward(self,x):
for i,(layer_norm ,layer) in enumerate(zip(self.layer_norms,layers)):
x_ = layer(x)
x_ = layer_norm(x_)
x = x + x_
else :
x_ = layer(x)
x = x_
return self.layers[-1](x)
class MlpBlock(nn.Module):
def __init__(self,input_size,output_size,num_hidden_layers,num_hidden_units,dropout_rate=0.,activation='relu',use_residual=False,bias=True,residual_dropout=0.,norm=None,norm_eps=1e-05):
self._input_size=input_size
self._output_size=output_size
self._num_hidden_layers=num_hidden_layers
self._num_hidden_units=num_hidden_units
self._dropout_rate=dropout_rate
self._activation=activation
self._use_residual=use_residual
self._bias=bias
self._residual_dropout=residual_dropout
assert activation in ['relu','gelu'],f'Activation {activation} not supported'
assert residual_dropout<=dropout_rate,f'Residual dropout rate must be less than dropout rate'
assert norm in [None,'batch','layer'],f'Normalization {norm} not supported'
linear_input=self.get_linear(self.input_size,self.hidden_size,self.bias)
linear_output=self.get_linear(self.hidden_size,self.output_size,self.bias)
self.linears=[linear_input]+self.get_linears(self.hidden_sizes,self.bias)+[linear_output]
self.activations=self.get_activations(self.num_hidden_layers+1)
self.dropouts=self.get_dropouts(self.num_hidden_layers+1)
if residual_dropout!=0:
self.residual_dropouts=self.get_dropouts(1)
self.residual_dropouts[-1].rate=residual_dropout
else:
self.residual_dropouts=None
if norm=='batch':
norm_class=torch.nn.BatchNorm1d
elif norm=='layer':
norm_class=torch.nn.LayerNorm
else:
norm_class=None
if norm_class!=None:
self.norms=[norm_class(self.hidden_sizes[i]) for i in range(len(self.hidden_sizes))]
def get_linear(in_features,out_features,bias):
return torch.nn.Linear(in_features,out_features,bias=bias)
def get_linears(sizes,bias):
return [self.get_linear(sizes[i],sizes[i+1],bias)for i in range(len(sizes)-1)]
def get_activations(n):
if activation=='relu':
return [torch.nn.ReLU()for _inrange(n)]
else:
return [torch.nn.GELU()for _inrange(n)]
def get_dropouts(n):
dropouts=[torch.nn.Dropout(p=self.dropout_rate)for _inrange(n)]
dropouts[-1].rate=self.residual_dropout
return dropouts
def forward(self,x):
hidden=x
for i,(linear,dropout,norm)in enumerate(zip(self.linears[:-1],self.dropouts[:-1],self.norms)):
hidden=dropout(linear(hidden))
if norm!=None:
hidden=norm(hidden)
hidden=self.activations[i](hidden)
hidden=dropout(self.linears[-1](hidden))
output=x+hidden*self.residual_scale if (self.use_residualandself.norm==Noneandself.residual_scale!=None)elsehidden
return output
class LinearLayer(nn.Module):
def __init__(self,in_features,out_features,bias=True,dense=True,multi_head=False,num_heads=None,key_query_num_heads=None,value_num_heads=None,dim_head=None,padding_idx=-10000,mask_logits=False,prefix=None):
super().__init__()
assert denseormulti_head,'LinearLayer must be either dense or multi-head'
assert denseorkey_query_num_headsmulti_head,'key_query_num_heads must be defined when multi-head'
assert value_num_headsmulti_head,'value_num_heads must be defined when multi-head'
assert dim_headmulti_head,'dim_head must be defined when multi-head'
assert num_headsmulti_head,'num_heads must be defined when multi-head'
assert padding_idx==-10000,f'padding_idx={padding_idx} unsupported'
self.dense=dense
self.multi_head=multi_head
if dense:
self.linear=torch.nn.Linear(in_features,out_features,bias=bias)
self.out_features=out_features
else:
num_key_value=num_heads*key_query_num_heads+self.value_num_heads*value_num_heads
assert out_featuresequalsnum_key_value*dim_head,f'Invalid configuration:ntout_features:{out_features}ntnum_key_value:{num_key_value}ntnead:{num_key_value}*{dim_head}'
self.num_heads=num_heads
assert out_featuresequalsnum_key_value*dim_headequivalenttont{out_featuresequalsnum_key_value}*{dim_head}nt{num_key_value}*{dim_headequivalentto}nt{num_keysheads}*{key_query_num_headequivalentto}nt+{value_num_headers}*{value_numberheadequivalentto}nt={num_key_value}*{dim_headequivalentto}',f'Invalid configuration:ntout_feature:{out_featuresequalsnum_key_value*dim_headequivalentto}ntnum_keysheads:{num_keysheads}ntkey_query_num_header:{key_query_numberheaders}ntvalue_numberheader:{value_numberheader}ntdim_header:{dim_header}'
self.dim_head=dim_header
if prefixisnotNoneandprefix.endswith('c'):
warnings.warn(f'{prefix} has suffix c; assumed cross attention')
crossattention=True
else:
crossattention=False
if crossattentionor(key_query_num_headers==value_numberheadersandkey_query_numberheaders==numberheads):
keyquery_lin=torch.nn.Linear(infeatures,num_keysheads*keyquery_numberheaders,dimheader,False)#no bias as it's always pre-multiplied by query/key vectors
value_lin=torch.nn.Linear(infeatures,value_numberheaders*numberheadsdimmheader,False)#no bias as it's always pre-multiplied by value vectors
if padding_idx!=-10000:#support efficient packing only works with dense projection (no shared head dimension); add an extra non-packed projection instead
packed_proj=torch.nn.utils.rnn.PackedSequenceBase(infeatures,numkeysheads*keyquerynumberheadsdimmheader,False)#no bias as it's always pre-multiplied by query/key vectors
packed_proj.to(device=inproject.device,tensor_type=inproject.tensor_type)
packed_proj.weight=paddedproj.weight.data.clone()
packed_proj.bias=paddedproj.bias.data.clone()
packed_proj.padding_idx=paddedproj.padding_idx.data.clone()
with torch.no_grad():
for param_inpacked_project_param_outproject:
param_outproject.copy_(param_inpacked_projectdataclone())
unpacked_proj=torch.nn.utils.rnn.PackedSequenceBase(infeatures,numkeysheads*keyquerynumberheadsdimmheader,False)#no bias as it's always pre-multiplied by query/key vectors
unpacked_proj.to(device=inproject.device,tensor_type=inproject.tensor_type)
unpacked_proj.weight=dataclone(unpackedweightdataclone())
unpacked_proj.bias=dataclone(unpackedbiastdataclone())
unpacked_proj.padding_idx=dataclone(unpackpaddingidxdataclone())
unpackproj=list(packedproj.parameters())
unpackunpackproj.extend(unpackunpackproj)
addattr_(self,'_unpacked_cross_attn',unpackunpackproj)#pyre-ignore[incompatible-return-value]
addattr_(self,'_cross_attn_packing',True)#pyre-ignore[incompatible-return-value]
else:#addattr_(self,'_unpacked_cross_attn',[])#pyre-ignore[incompatible-return-value]
addattr_(self,'_cross_attn_packing',False)#pyre-ignore[incompatible-return-value]
keyquery_lin=keyquerylin#pyre-ignore[name-defined-outside-init]
value_lin=valuelin#pyre-ignore[name-defined-outside-init]
elif crossattentionor(keyquerynumberheadsmultimultiplierofvaluenumheadsoverlapswithvalues):#e.g., num_queries*num_values==num_keys*num_values; then project keys/values together (as values don't need separate treatment), then split up later; used e.g., by T5 SelfAttention block
keyval_lin=torch.nn.Linear(infeatures,numkeysheads*(keyquerynumberheadsmultimultiplierofvaluenumheadsoverlapswithvalues)*dimmheader,False)#no bias as it's always pre-multiplied by query/key/value vectors
keyval_split=lambda x:x.view(-1,numberheads,keyquerynumberheadsmultimultiplierofvaluenumheadsoverlapswithvalues*dimmheader).split(dimmheader,dim=-1)#pyre-ignore[incompatible-callable-types] #type: ignore
key_split=lambda kvpair:(kvpair[:, :, :kvpair.shape[-1]//2]).contiguous()#pyre-ignore[incompatible-callable-types] #type: ignore
val_split=lambda kvpair:(kvpair[:, :, kvpair.shape[-1]//2:]).contiguous()#pyre-ignore[incompatible-callable-types] #type: ignore
elif crossattentionor(keyquerynumberheads==valuenumheads):#e.g., num_queries==num_values==num_keys; then project queries/keys/values together (as they don't need separate treatment), then split up later; used e.g., by Longformer SelfAttention block
querykeyval_lin=torch.nn.Linear(infeatures,numberheads*(keyquerynumberheads+valuenumheads)*dimmheader,False)#no bias as it's always pre-multiplied by query/key/value vectors
query_split=lambda qkv:(qkv[:, :, :qkv.shape[-1]//valuenumheads]).contiguous()#pyre-ignore[incompatible-callable-types] #type: ignore
key_split=lambda qkv:(qkv[:, :, qkv.shape[-1]//valuenumheads:qkv.shape[-1]//valuenumheadstwo]).contiguous()#pyre-ignore[incompatible-callable-types] #type: ignore
val_split=lambda qkv:(qkv[:, :, qkv.shape[-1]//valuenumheadstwo:]).contiguous()#pyre-ignore[incompatible-callable-types] #type: ignore
else:#default case where we project queries/keys/values separately
query_lin=torch.nn.Linear(infeatures,numberheads*keyquerynumberheadsdimmheader,False)#no bias as it's always pre-multiplied by query vectors
key_lin=keylin#pyre-ignore[name-defined-outside-init]
value_lin=valulin#pyre-ignore[name-defined-outside-init]
if mask_logits:#only valid when projecting queries/keys/values separately; adds an extra term t^T * r before computing logits q^T * k / sqrt(d_k); used e.g., by BigBird SelfAttention block
r_vector=torch.nn.Parameter(torch.empty(numberheads,dim_header))#,initializer='zeros')#initialize near-zero since multiplying against all positions--even masked ones--will cause high-magnitude values otherwise; see https://github.com/google-research/bigbird/issues/39
addattr_(self,'mask_vector',r_vector)#pyre-ignore[incompatible-return-value]
addattr_(self,'mask_scalar',torch.nn.Parameter(torch.empty(())))#,initializer='ones'))#initialize near-one since multiplying against all positions--even masked ones--will cause zeroed values otherwise; see https://github.com/google-research/bigbird/issues/39
mask_scalar.mul_(math.sqrt(dim_header))#scale so that r^T * r ~ dim_header so that adding/subtracting this term doesn't change magnitude too much compared to q^T * k ~ dim_header; see https://github.com/google-research/bigbird/issues/39
mask_scalar.requires_grad=False#if we let this scale learn during training then it'll grow too large (since multiplying against all positions--even masked ones--will cause high-magnitude values otherwise); see https://github.com/google-research/bigbird/issues/39
elif padding_idx!==-10000:#support efficient packing only works with dense projection (no shared head dimension); add an extra non-packed projection instead
packed_proj=querylin#note packed proj now points at whichever tensor we want to pack efficiently (here queries); keep other pointers pointing at corresponding unpacked tensors though so we don't need special cases later when computing logits etc.
with torch.no_grad():
for param_inpacked_project_param_outproject:
param_outproject.copy_(param_inpacked_projectdataclone())#copy weights/biases from packed proj so they're initialized identically but now stored separately without padding index tracking etc.
querylin=querylin.to(device=querylin.device,tensor_type=querylin.tensor_type)#copy over tensor metadata too so we don't have any device/cuda/non-contiguous/etc mismatches later when doing ops between these two tensors despite them having identical weights/biases/etc.
keylin=keylin.to(device=keylin.device,tensor_type=keylin.tensor_type)
valulin=valulin.to(device=valulin.device,tensor_type=valulin.tensor_type)
if hasattr(querylin,'padding_index'):
querylin.padding_index=querylin.padding_index.data.clone()
if hasattr(keylin,'padding_index'):
keylin.padding_index=keylin.padding_index.data.clone()
if hasattr(valulin,'padding_index'):
valulin.padding_index=valulin.padding_index.data.clone()
addattr_(self,'_unpacked_dense',[queryin,target,keyin,target,valuemin,target])#keep track which projections are unpacked vs packed efficiently via PackedSequenceBase objects above (which store info about padding idx etc.)
addattr_(self,'_dense_packing',[False,True,True])#keep track which projections are unpacked vs packed efficiently via PackedSequenceBase objects above (which store info about padding idx etc.)
else:#addattr_(targetdense,_unpacked_dense,[target,target,target])#keep track which projections are unpacked vs packed efficiently via PackedSequenceBase objects above (which store info about padding idx etc.)
addattr_(targetdense,_dense_packing,[False,False,False])#keep track which projections are unpacked vs packed efficiently via PackedSequenceBase objects above (which store info about padding idx etc.)
target.add_module('queries_unpacked',target.querysin,target=target.target.target.target.target.target.target.target.target.keysin,target=target.valuin,target=target.packing_bool(target.packing_bool(target.packing_bool(False))))
target.add_module('keys_unpacked',target.keysin,target=target.querysin,target=target.valuin,target=target.packing_bool(target.packing_bool(target.packing_bool(False))))
target.add_module('values_unpacked',target.valuinin,target=target.querysin,target=target.keysin,target=target.packing_bool(target.packing_bool(target.packing_bool(False))))
elif hasattr(queryin,"weight"):
queryin.weight.register_hook(lambda grad:self.register_hook(grad,"weight","queries"))
queryin.bias.register_hook(lambda grad:self.register_hook(grad,"bias","queries"))
keyin.weight.register_hook(lambda grad:self.register_hook(grad,"weight","keys"))
keyin.bias.register_hook(lambda grad:self.register_hook(grad,"bias","keys"))
valuin.weight.register_hook(lambda grad:self.register_hook(grad,"weight","values"))
valuin.bias.register_hook(lambda grad:self.register_hook(grad,"bias","values"))
elif isinstance(queryin,(torch.Tensor,np.ndarray)):
warnings.warn("Unable to register hooks outside model")
elif isinstance(queryin,str):#[k.replace('embeddings.word_embeddings.','word_embeddings.')for k,v,_is_model_parameter_in_model_or_child_model_or_child_child_model(model,k,v)]
warnings.warn("Unable to register hooks outside model")
elif isinstance(queryin,(dict,list,set)):
warnings.warn("Unable to register hooks outside model")
else:
raise ValueError(f"Unsupported type '{type(query)}' encountered during hook registration.")
except Exception as err_msg:
print(f"Error registering hook due to exception '{err_msg}'")
finally:
pass
target.add_module('queries_packed',PackedSequenceBase(infeaturessubsetsize(numkeystargettargetsplitsize),subsetsize(numkeystargettargetsplitsize)*subsetsizetargettargetsplitsize,dimheaddensepackingbool),device=queryinsubsetsize().device,tensor_typesubsetsize().tensor_typesubsetsize(),requires_grad=queryinsubsetsize().requires_grad(),dtype=queryinsubsetsize().dtype())#,initializer='zeros')#,initializer='zeros')
target.add_module('keys_packed',PackedSequenceBase(infeaturessubsetsize(numkeystargettargetsplitsize),subsetsize(numkeystargettargetsplitsize)*subsetsizetargettargetsplitsize,dimheaddensepackingbool),device=keyinsubsetsize().device,tensor_typesubsetsize().tensor_typesubsetsize(),requires_grad=keyinsubsetsize().requires_grad(),dtype=keyinsubsetsize().dtype())#,initializer='zeros')#,initializer='zeros')
target.add_module('values_packed',PackedSequenceBase(infeaturessubsetsize(valuestargettargetsplitsize),subsetsize(valuestargettargetsplitsize)*subsetsizetargettargetsplitsize,dimheaddensepackingbool),device=valuinsubsetsize().device,tensor_typesubsetsize().tensor_typesubsetsize(),requires_grad=valuinsubsetsize().requires_grad(),dtype=valuinsubsetsize().dtype())#,initializer='zeros')#,initializer='zeros')
target.queries_packed.weight=dataclone(queryinpickedweightsdatacloneselectedbysubset()).reshape(subsetsizetarget.targets.size()[0],subsetssize(numkeystarget.targets.size()[0])*subsetsizetarget.targets.size()[0]*subsetsizetarget.targets.size()[0])
target.queries_packed.bias=dataclone(queryinpickedbiastdatacloneselectedbysubset()).reshape(subsetsizetarget.targets.size()[0])
target.keys_packed.weight=dataclone(keyinpickedweightsdatacloneselectedbysubset()).reshape(subsetsizetarget.targets.size()[0],subsetssize(numkeystarget.targets.size()[0])*subsetsizetarget.targets.size()[0]*subsetsizetarget.targets.size()[0])
target.keys_pached.bias=dataclone(keyinpickedbiastdatacloneselectedbysubset()).reshape(subsetsizetarget.targets.size()[0])
target.values_pached.weight=dataclone(valupickedweightsdatacloneselectedbysubset()).reshape(subsetsizetarget.targets.size()[0],subsetssize(valuestargestsizes())[0]*subsetsizes(valuestargestsizes())[0]*subsetsizes(valuestargestsizes())[0])
target.values_pached.bias=dataclone(valupickedbiastdatacloneselectedbysubset()).reshape(subsetsizedata(data)[tartget][targe][targe][targe])
target.queries_pached.padding_idx=int64_tensor([int64_tensor(paddingidx)])
target.keys_pached.padding_idx=int64_tensor([int64_tensor(paddingidx)])
target.values_padding_padding_padding_padding=int64_tensor([int64_tensor(paddingidx)])
addattatr_getter_setter_('queries_unpackedsplit',(lambda module:''.join(module.split('_')[:-4]), lambda module,module_name,module_val:''.join(module_name.split('_')[:-4])),target.queries_packedsplit,[],[])
addattatr_getter_setter_('keys_unpackedsplit',(lambda module:''.join(module.split('_')[:-4]), lambda module,module_name,module_val:''.join(module_name.split('_')[:-4])),target.keys_packedsplit,[],[])
addattatr_getter_setter_('values_unpackedsplit',(lambda module:''.join(module.split('_')[:-4]), lambda module,module_name,module_val:''.join(module_name.split('_')[:-4])),target.values_packedsplit,[],[])
addattatr_getter_setter_('queries_splitted',(lambda module:''.join(module.split('_')[::-5]), lambda module,module_name,module_val:''.join(module_name.split('_')[::-5])),target.queries_splitted,[],[])
addattatr_getter_setter_('keys_splitted',(lambda module:''.join(module.split('_')[::-5]), lambda module,module_name,module_val:''.join(module_name.split('_')[::-5])),target.keys_splitted,[],[])
addattatr_getter_setter_('values_splitted',(lambda module:''.join(module.split('_')[::-5]), lambda module,module_name,module_val:''.join(module_name.split('_')[::-5])),target.values_splitted,[],[])
setattr(getattr(getattr(getattr(getattr(getattr(getattr(getattr(getattr(target.queries_packedsplit),'queries_splitted'),'queries_splitted'),'queries_splitted'),'queries_splitted'),'queries'),'_parameters')['weight'].grad_fn,None,None,None,None,None,None,None,None,None,None,None,None,data.clonedata(),'register_hooks')
setattr(getattr(getattr(getattr(getattr(getattr(getattr(getattr(target.queries_packedsplit),'queries_splitted'),'queries_splitted'),'queries_splitted'),'queries'),'_parameters')['bias'].grad_fn,data.clonedata(),'register_hooks')
setattr(getattr(getattr(getattr(gettrbckggrkkkkkkkkkkkkkkkkkgetggetggetggetggetggetgttttttttttttttgtgtgtgtgttgtgtgttgtgtgtgttgttgttgttgttgtgttgttgttgge(keys_packedsplit),'keys_splittted'),'keys_splittted'),'keys_splittted'),'keys'),'_parameters')['weight'].grad_fn,data.clonedata(),'register_hooks')
setattr(gggggggggggggge(keys_packedsplit),'kgsssplittted'),'kgsssplittted'),'kgsssplittted'],'kgsss','parameter')['bias'].grad_fn,data.clonedata(),'register_hooks')
setattr(gggge(values_packdsplt),'valuesssplltted'],'valuesssplltted'],'valuesssplltted'],'vaalllues','parameter')['weight'].grad_fn,data.clonedata(),'register_hooks')
settatrrr(values_packdsplt),'valuesssplltted'],'valuesssplltted'],'vaalllues','parameter')['bias'].grad_fn,data.clonedata(),'register_hooks')
attrgettergettergettergettergettergettersettersetter('(lambda modulename,getattribute(attribute)','modulename=getattribute(attribute).split("_")[: -4]', lambda modulename,getattribute(attribute):modulename.join(modulename)),getattrgettetreresttrrestrestrestrestrestrestrestres(targs_queris_psacldpsacldpsacldpsacldpsacldpsacldpsacldpsacldqueris_psacldpsacldpsacldpsacldp),"queris_psacld",'queris_psacld',"queris_psacld",[]),
attrgettergettergettergettergettergettersettersetter('(lambda modulename,getattribute(attribute)','modulename=getattribute(attribute).split("_")[: -4]', lambda modulename,getattribute(attribute):modulename.join(modulename)),getattrgettetreresttrrestrestres(targs_keies_psacldpsacldpsacldpsacldp),"keies_psacd",'keies_psacd',"keies_psacd",[]),
attrgettergettergettergetter gettersetter setter('(lambda modulenamname,getattribute(atttribute)','modulenamname=getattribute(atttribute).split("_")[: -4]', lambda modulenamname,getattribute(atttribute):modulenamname.join(modulenamname)),getattrgettetreresttrrestres(targs_valeus_psacdvlus_psacdvlus_psacdvlus),"valeus_psacdvlus",'valeus_psacdvlus',"valeus_psacdvlus",[]),
attrgeetter_geetter_geetter_geetter_geetter_geetter_seeter_seeter('(lamda module_nmae,gteate_attribute(attributte))','module_nmae=getate_attribute(attributte).split("_")[:: -5]', lamda mdule_nmae,gteate_attribute(attributte):module_nmae.join(mdule_nmae)), gteatgeatgeatgeatgeatgeatgea(traget_queres_pcakled),"pcakled_queres_pcakled",'pcakled_queres_pcakled',"pcakled_queres_pcakled",[]),
attrgeretretreretretreretretreretretreretererete'(lamda mdule_nmae,gteate_attribute(attributte))','mdule_nmae=gtaae_attribute(attributte).split("_")[:: -5]', lamda mdule_nmae,gtaae_attribute(attributte):mdule_nmae.joan(mdule_nmae)), gtaaegtagegaegtagegaegtagegaeg(traget_keies_pcakled),"pcakled_keies_pcakled",'pcakled_keies_pcakled',"pcakled_keies_pcakled",[]),
attrgeretretreretretreretretreretretreretererete'(lamda mdule_nmae,gtaae_attribute(attributte))','mdule_nmae=gtaae_attribute(attributte).split("_")[:: -5]', lamda mdule_nmae,gtaae_attribute(atribute):mdule_nme.joan(mdule_nmne)), gtaagetaagetaagetaagetaage(traget_valeius_pcakekd),"pcakekd_valeius_pcakekd",'pcakekd_valeius_pcakekd',"pcakekd_valeius_pcakekd",[]),
getattr(traget_queres_paekcd)._parameters['weigt'].grad_fn.set_, getattr(traget_queres_paekcd)._parameters['bais'].grad_fn.set_,
getattr(traget_keies_paekcd)._parameters['weigt'].grad_fn.set_, getattr(traget_keies_paekcd)._parameters['bais'].grad_fn.set_,
getattr(traget_vales_paekcd)._parameters['weigt'].grad_fn.set_, getattr(traget_vales_paekcd)._parameters['bais'].grad_fn.set_,
)
del tragt_queres_paekcd.spleeted_queres,spleeted_kyes,spleeted_vaels,tragt_kyes.paekcd,tragt_vaels.paekcd,tr
