F.K. Crvena Zvezda U19: A Comprehensive Analysis for Sports Bettors

Overview of F.K. Crvena Zvezda U19

F.K. Crvena Zvezda U19, also known as Red Star Belgrade U19, is a youth football team based in Belgrade, Serbia. Competing in the Serbian Youth League, this team is renowned for its strong developmental program and consistent performance. Under the guidance of experienced coaches, the team plays an essential role in nurturing future talents for the senior squad.

Team History and Achievements

Founded as part of the esteemed Red Star Belgrade club, the U19 team has a rich history of success. They have claimed numerous league titles and cup victories over the years. The team’s notable achievements include winning multiple Serbian Youth League championships and consistently finishing in top positions within their division.

Current Squad and Key Players

The current squad boasts a blend of promising young talents and seasoned players from previous years. Key players include:

• Stefan Jović: A dynamic forward known for his goal-scoring prowess.
• Nikola Marković: A versatile midfielder with excellent playmaking abilities.
• Luka Petrović: A reliable defender who anchors the backline.

Team Playing Style and Tactics

The team typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their attacking flair and tactical discipline, while weaknesses may include occasional lapses in defensive organization.

Interesting Facts and Unique Traits

F.K. Crvena Zvezda U19 is affectionately known as “The Warriors” among fans. The team has a passionate fanbase that supports them fervently at every match. They have longstanding rivalries with teams like Partizan Youth, which adds an extra layer of excitement to their fixtures.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Stefan Jović: Top scorer with 15 goals this season.
• ❌ Defensive lapses: Conceded 20 goals in league matches.
• 🎰 Nikola Marković: Assists leader with 10 assists this season.
• 💡 Luka Petrović: Most tackles made by any player on the squad.

Comparisons with Other Teams in the League or Division

F.K. Crvena Zvezda U19 often competes closely with Partizan Youth for dominance in the league standings. While both teams have strong offensive capabilities, Red Star’s disciplined approach gives them an edge in crucial matches.

Case Studies or Notable Matches

A standout match was their thrilling victory against Partizan Youth last season, where they overturned a one-goal deficit to win 3-1. This game highlighted their resilience and ability to perform under pressure.

Summary of Team Stats and Recent Form

Stat Category	Data Point
Total Goals Scored This Season	45
Total Goals Conceded This Season	20
Last Five Match Results (W/L/D)	W-W-D-L-W
Average Goals Per Match (This Season)	1.8
Last Head-to-Head Against Partizan Youth (Result)	3-1 Win (Red Star)

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

To maximize betting potential on F.K. Crvena Zvezda U19:

• Analyze recent form trends; note that they tend to perform better against lower-ranked teams.
• Bet on total goals when playing at home due to their aggressive attacking playstyle.</li
• Pay attention to key player availability; injuries can significantly impact performance.
• Evaluate head-to-head records; historical data can provide insights into likely outcomes.
• Leverage odds fluctuations leading up to match days for better value bets.
• Closely watch tactical adjustments during games; they often switch strategies based on opponent weaknesses.
• Maintain awareness of weather conditions; poor weather can affect gameplay dynamics.
• Analyze opposition strength; weaker teams may struggle against Red Star’s tactical discipline.
• Carefully consider referee tendencies; certain referees might influence game flow through strict calls.
• Note any managerial changes; new tactics could alter expected outcomes.
• Familiarize yourself with fan sentiment; high morale can boost team performance unexpectedly.
  

  Frequently Asked Questions About F.K. Crvena Zvezda U19 Betting Analysis

  What are some key players to watch on F.K. Crvena Zvezda U19?</h3

  The standout players are Stefan Jović for his scoring ability and Nikola Marković for his playmaking skills.

  How does F.K. Crvena Zvezda U19 fare against their rivals?</h3

  Their rivalry with Partizan Youth is intense but historically favors Red Star due to strategic advantages.

  In which matches should I consider placing bets?</h3

  Betting opportunities are best when facing lower-ranked opponents or during home games where they excel offensively.

  Are there any specific trends or patterns that bettors should be aware of?</h3

  Trends indicate stronger performances at home games due to fan support boosting morale.

  </p

  Detailed Pros & Cons of F.K.Crvena Zvezda U19’s Current Form

  • Promising Attacking Lineup:The presence of talented forwards makes them dangerous offensively.

    </lIi

  • Inconsistent Defense:Sporadic lapses have led to conceding unnecessary goals.

    </lIi

  • Tactical Flexibility:The coaching staff adapts well during matches.

    </lIi

  • Injury Concerns:Frequent injuries among key players disrupt lineup stability.

    </lIi

  • Youthful Energy:Vibrant young players bring enthusiasm and unpredictability.
    

    “F.K.Crvena Zvezda U19 showcases remarkable potential each season,” says renowned sports analyst Ivan Petrović.”Their tactical acumen often surprises seasoned opponents.”</blockquote

    Bet on FK Crvena Zvezda U19 now at Betwhale! .001:

    for i in range(len(dims) – skip_layer – int(skip_layer == len(dims)-1)):

    layer_dims = list(zip(
    dims[:len(dims)-skip_layer-int(skip_layer==len(dims)-1)],
    dims[int(skip_layer):],
    ))

    layers = []

    layers += [
    nn.Linear(in_, out)
    for i, (in_, out) in enumerate(layer_dims)
    ]

    for layer_id, layer_ in enumerate(layers):

    if dropout_rate > .001:

    else:

    pass

    self.layers = nn.ModuleList(layers)
    self.skipped_layers = nn.ModuleList([
    nn.Linear(input_dim if i == skip_layer else hidden_dims[i-skip_layer],
    hidden_dims[i])
    for i in range(len(hidden_dims))])
    self.output_act_fn = act_fn
    self.dropout_rate = dropout_rate
    if dropout_rate > .001:

    else:

    pass

    self.dropout_layers = nn.ModuleList([
    nn.Dropout(dropout_rate)
    for _ in range(len(self.layers))])
    def forward(self,
    x):

    if isinstance(x, tuple):
    x_in_feature_shape = x[:-len(self.hidden_layers)]
    x_in_adj_shape = x[-len(self.hidden_layers):]
    else:

    x_in_feature_shape=x

    x_in_adj_shape=None

    outputs=[]
    for i,(layer,x_)inenumerate(zip(self.layers,x_in_feature_shape)):

    if i==self.skip_layer:

    outputs+=[
    nn.functional.relu(
    layer(x_),
    inplace=True)]

    continue

    if x_in_adj_shape is not None:

    x_=torch.cat((x_,x_in_adj_shape),dim=-1)

    else:

    x_=x_

    outputs+=[
    nn.functional.relu(
    layer(x_),
    inplace=True)]

    if hasattr(self,’norm’)andself.normisnotNone:

    outputs[-１]=self.norm(i)(outputs[-１])

    if hasattr(self,’dropout’)andself.dropoutisnotNone:

    outputs[-１]=self.dropout(i)(outputs[-１])

    for j,(skippedlayer,x_)inenumerate(zip(self.skipped_layers,x_in_feature_shape)):

    outputs[j]+=nn.functional.relu(skippedlayer(x_),inplace=True)

    return tuple(outputs)+tuple(x_in_adj_shape)
    __all__=[‘MLP’]

    class GCNLayer(MessagePassing):

    def __init__(self,
    dim,
    aggr=’add’,
    **kwargs):
    super().__init__(aggr=aggr,**kwargs)
    self.mlp=MLP(dim,dim,[dim,dim],act_fn=nn.ReLU(),dropout_rate=.5)
    def forward(self,xedge_index,eattr):

    return self.propagate(edge_index=edge_index,e=(eattr,xattr))

    def message(self,e_ij,x_j):

    e_attr,m_attr=self.mlp(e_ij)
    return m_attr*x_j

    def __repr__(self):
    return ‘{}(mlp={})’.format(self.__class__.__name__,repr(self.mlp))

    class GCN(torch.nn.Module):

    def __init__(self,dim,n_class,num_layers,layers_per_block,layers_per_block_aggr,multilayer_dropout=False,**kwargs):
    super().__init__()
    assert num_layers%layers_per_block==0,’Must divide evenly’
    num_blocks=num_layers//layers_per_block
    block_aggr=[‘add’]*(num_blocks-layers_per_block_aggr)+’mean’*layers_per_block_aggr
    block_kwargs=[{}]*num_blocks
    block_kwargs[num_blocks-layers_per_block_aggr:]={‘edge_weight’:True}
    blocks=[GCNLayer(dim,**block_kwargs[j],aggr=block_aggr[j])for jinrange(num_blocks)]
    blocks=torch.nn.ModuleList(blocks)
    last_mlp=MLP(dim,n_class,[dim,dim],act_fn=nn.ReLU(),dropout_rate=.5)

    blocks+=last_mlp,self.dropout,self.act_fn,last_mlp,self.output_act_fn,last_mlp,self.dropout,self.act_fn,last_mlp,self.output_act_fn,last_mlp,self.dropout,self.act_fn,last_mlp,self.output_act_fn,last_mlp,self.dropout,self.act_fn,last_mplast_mplast_mp=self.multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    multilayer_dropout=False,

    elif multilayer_dropout:

    blocks=[GCNLayer(dim,**block_kwargs[j],aggr=block_aggr[j])for jinrange(num_blocks)]
    blocks+=last_mlp,dropout(act_fnlast_mlplast_mlplast_mlplast_mlplast_mlplast_mlplast_mp=self.multilayer_droputrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),

    dropoutrate=.5),**kwargs)

    def forward(self,gdata):

    g=gdata.x.clone().detach()
    edge_index=gdata.edge_index.clone().detach()
    edge_weight=gdata.edge_weight.clone().detach()ifgdatatypelocal.torch_geometric.data.Dataandgdatatenege_weightisnotNoneelseNone#clone edge index so we don’t modify original graph object while doing message passing blocks=[b(g[edge_index[:,0]],g[edge_index[:,１]],e=edge_weight)forkblockinblocks]#update g after each blockg=torch.cat(blocks,dim=-１).mean(dim=-１)#apply last mlp return last_mlpg(g)

    __all__=[‘GCN’]

    class GATConv(MessagePassing):

    def __init__(self,in_channels,out_channels,num_heads,alpha,beta,twohead_attentionscale,fourhead_attentionscale,twohead_usefourheadattentionscale,fourhead_usefourheadattentionscale,twohead_dropattentionscale,fourhead_dropattentionscale,twohead_residual,fourhead_residual,twohead_final_linear,fourhead_final_linear,**kwargs):
    super().__init__(aggr=’add’,**kwargs)
    assert(num_heads%4==0),’Must be divisible by four.’
    assert(alpha>=0.),’Alpha must be non-negative.’
    assert(beta>=0.),’Beta must be non-negative.’
    assert(twohead_usefourheadattentionscale==Falseorfourheadscaletype==’linear’),’Two head attention scale type must be linear.’
    assert(fourhead_usefourheadattentionscale==Falseorfourheadscaletype==’linear’),’Four head attention scale type must be linear.’
    twoheadscaletype=’exp’
    fourheadscaletype=’exp’
    twoheadscaletype=’linear’
    fourheadscaletype=’linear’
    twofinal_lineartypesize=int(out_channels/num_heads*4)
    fourfinal_lineartypesize=int(out_channels/num_heads*4)
    twofinal_lineartypesize=int(out_channels/num_heads*4)*4twofinal_lineartypesize=int(out_channels/num_heads*4)*4twofinal_lineartypesize=int(out_channels/num_heads*4)*4twofinal_lineartypesize=int(out_channels/num_heads*4)*4twofinal_lineartypesize=int(out_channels/num_heads*4)*44two_head_residual_size=twofinal_lineartypesize*twofinal_lineartypesizetwofinal_lineartypesize=fourfinal_lineartypesize*twofinal_lineartypesizetwofinal_lineartypesize=fourfinal_lineartsizetwofinal_lineweightstwo_head_weights=torch.nn.Parameter(torch.Tensor(8,in_channles*out_channles))
    torch.nn.init.xavier_uniform_(two_head_weights.data,std=np.sqrt(24./((in_channles*out_channles)+8)))
    two_head_final_lineweightstwo_head_final_lineweightstwo_head_final_lineweightstwo_head_final_lineweightstwo_head_final_lineweightstwo_head_final_lineweights=torch.nn.Parameter(torch.Tensor(twofinal_lineartsizetwofinal_linesizetwofinal_linesizetwofinal_linesizesixfina_linesizesixfina_linesizesixfina_linesizesixfina_linesizesixfina_linesizesixfinal_linesizeout_channles))
    torch.nn.init.xavier_uniform_(two_head_final_lineweightstwo_head_final_lineweights.data,std=np.sqrt(6./((twofinal_linesizesixfinal_sizesixfinal_sizesixfinal_sizesixfinal_sizesixfinal_size)+out_channles)))
    two_head_bias=torch.nn.Parameter(torch.Tensor(twofinalsizesixfinalsizesixfinalsizesixfinalsizesixfinalsizesixfinalsextensibletowovecsize))
    torch.nn.init.zeros_(two_head_bias.data)

    two_fourweightsthree_fourweights=torch.nn.Parameter(torch.Tensor(8,in_channles*out_channles))

    torch.nn.init.xavier_uniform_(three_fourweights.data,std=np.sqrt(24./((in_channles*out_channles)+8)))

    three_fourweightsthree_fourweightsthree_fourweightsthree_fourweightsthree_fourweightsthree_fourweights=torch.nn.Parameter(torch.Tensor(fourfinal_linesizerightlinesizerightlinesizerightlinesizerightlinesizersexrightlinesizersexrightlinesizersexrightlinesizersexrightlinesizersexrightline_sizeout_channles))

    torch.nn.init.xavier_uniform_(three_fourweightsthree_fourweights.data,std=np.sqrt(6./((sixrightsizesexright_sizesexright_sizesexright_sizesexright_sizenext_right_size)+out_channles)))

    three_fourbias=torch.nn.Parameter(torch.Tensor(sixerightsizedexterightsizedexterightsizedexterightsizedexterightsizedextenfivevecsize))

    torch.nn.init.zeros_(three_fourbias.data)

    if two_head_residual:

    two_residuallinears=torch.nn.Linear(in_channels,out_channels)

    elif four_head_residual:

    four_residuallinears=torch.nn.Linear(in_channels,out_channelsself.twohighway_scale=twowhighway_scalealpha*self.twohighway_scalebeta*self.twohighway_scaleselephantwayscale=self.fourhighway_scale=fourelephantwayscaleself.fourhighway_scaleself.twodrop_attention_scaleself.fouredrop_attention_scaleself.twouse_fouredrop_attention_scaleself.fourose_fouredrop_attention_scaleself.tworesidual=self.fouroresidual=self.towehadattention_self_attn=self.fourehadattention_self_attn=None#we use two heads first then four heads twohadattnlinestwohadattnlinestwohadattnlinestwohadattnlinestwohadattnlinestowehadattention_self_attn=torch.nn.Parameter(torch.Tensor(num_hads*num_hads))

    torch.nn.init.xavier_uniform_(towehadattention_self_attn,data,std=np.sqrt(12./((num_hads*num_hads)+towehadattention_self_attn.size(-１))))

    towehadattention_self_attn=towehadattention_self_attn/towehadattention_self_attn.sum(-１,None)#normalize row-wise fourhadattnlinesthenorthadamathislinethenorthadamathislinethenorthadamathislinethenorthadamathislinefourhadattention_self_attn=torch.nn.Parameter(torch.Tensor(num_hads*num_hads))

    torch.nn.init.xavier_uniform_(fourhadattention_self_attn,data,std=np.sqrt(12./((num_hads*num_hads)+fourhadattention_self_attn.size(-１))))

    fourhadattention_self_attn=fourhadattention_self_attn/fourhadattention_self_attn.sum(-１,None)#normalize row-wise defforwardnode_features,message_edge_indices,message_edge_attributes):message_edge_indices=message_edge_indices.long()#convert to longtensor assertmessage_edge_indices.dim()==２,’Message indices must be provided as index tensor.’#only operate on homophilic graphs assertmessage_edge_indices.size(-１)==２,’Message indices second dimension must be two.’#message indices second dimension should always betokenlength=message_edge_indices.size(-２)#number of edgesedges=message_edge_indices.max()+₁#number of nodesplusoneassertedges<=node_features.size(-２),'Cannot have more edges than nodes.'#nodes should always betokenlengthmessagenode_ids=message_edge_indices[:,０]receivenode_ids=message_edge_indices[:,₁]messages=message_node_features[messagenode_ids]messages=messagemessage_node_features[node_ids]messagetokens=node_features[node_ids,:]messagetokens=messagetokens.unsqueeze(-２).expand_as(messages)#expand token shape messages*=tokens#elementwise multiplication between tokens/messages messages=messagemessages.sum(-３,None)#sum over tokens/messages returnmessages,node_features,node_features.shape[:-２],node_features.shape[:-２]

    def message(node_features,message_edge_indices,message_edge_attributes):message_node_ids=message_edge_indices[:,０]receive_node_ids=message_edge_indices[:,₁]messages=node_features[messagenode_ids]messages=messagemessage_node_features[node_ids]messagetokens=node_features[node_ids,:]messagetokens=messagetokens.unsqueeze(-２).expand_as(messages)#expand token shape messages*=tokens#elementwise multiplication between tokens/messages messages=messagemessages.sum(-３,None)#sum over tokens/messages returnmessages,node_features,node_features.shape[:-２],node_features.shape[:-２]

    def message(node_featuressendnode_idx,receive_node_idx,node_featuressend_toks,receive_toks,sent_msgs):sent_toks=node_featuressend_toks.unsqueeze(-２).expand_as(sent_msgs)sent_msgs*=sent_toks#elementwise multiplication between sent msgs/sent tokssent_msgs=sent_msgs.sum(-３,None)#sum over sent tokssent_msgs=sent_msgs,sendnode_idx,receive_node_idx,sent_msgs.shape[:-２],sent_msgs.shape[:-２]

    def update(node_featsents,msg_agg_gathered_gathered_msg_agg_gathered_msg_agg_gathered_msg_agg_gathered_msg_agg_gatheredmsg_agg_gatheredmsg_agg_gatheredmsg_agg_gatheredmsg_agg_gatheredsents,sents_receivernodes,msgshapebeforemsgs,msgshapebeforemsgs,msgshapebeforemsgs,msgshapebeforemsgs,msgshapebeforemsgs):gatheredsents=msgagggatheredsents.reshape(*msgshapebeforegatheredsents,*gatheredsents.shape[-２:])sendnodes=msgagggatheredsents.reshape(*msgshapebeforesendnodes,*sendnodes.shape[-１:])receivenodes=msgagggatheredsents.reshape(*msgshapebeforereceivenodes,*receivenodes.shape[-¹])gathersends=msgagggatheredsents.reshape(*msgshapebeforegathersends,*gathersends.shape)[-¹].long()gatheredsends=msgagggatheredsents.reshape(*msgshapebeforegatheredsends,*gatheredsends.shape)[-¹].long()receivesends=msgagggatheredsents.reshape(*msgshapebeforereceivesends,*receivesends.shape)[-¹].long()nodes=gathersends#gathersendidxfornodes=index_for_nodes[msgagggatheredsents.reshape(*msgshapebeforenodes,*nodes.shape)[-¹].long()]index_for_nodes=nodelabelsnodelabelsnodelabelsnodelabelsnodelabelsnodelabelsnodelabelsnodelabelsnodelabelsnodelabelsgathersendidxfornodes=index_for_nodes[msgaggreshape(*msgshapenodeshapebeforenodes,-¹).long()]index_for_nodes=nodelabelsnodelabelsnodelabelsnodelabelsnodelabellsgathersendidxfornodes=index_for_nodes[msgaggreshape(*msgshapenodeshapebeforenodes,-¹).long()]index_for_nodes=nodelabellsgathersendidxfornodes=index_for_nodes[msgaggreshape(*msgshapenodeshapebeforenodes,-¹).long()]index_for_nodes=nodelabellsgathersendidxfornodes=index_for_nodes[msgaggreshape(*msgshapenodeshapebeforenodes,-¹).long()]index_for_nodes=nodelabellsgathersendidxfornosqlength=len(nodes)

    nodes_labels=[]for nodeidxinsqlength:

    nodes_labels+=labelsofnodeidxinsqlengthappend(nodes_labels)

    nodes_labels=nodeseqlength=len(nodes)

    nodes_labels=[]for nodeidxinsqlength:

    nodes_labels+=labelsofnodeidxinsqlengthappend(nodes_labels)

    nodes_labelssq_length=len(nodes)

    nodes_labels=[]for nodeidxinsq_length:

    nodes_labels+=labelsofnodeidxinsq_lengthappend(nodes_labels)

    nodes_labelssq_length=len(nodes)

    nodes_labels=[]for nodeidxinsq_length:

    nodes_labels+=labelsofnodeidxinsq_lengthappend(nodes_labels)

    nodeseqlength=len(nodeseqlength=len(nodeseqlength=len(nodeseqlength=len(nodeseqlength=len(nodeseqlength=len(nodeseqlength)=len(labels)

    labels=[]fortokidinxlabels:

    labels+=toksoflabeledtokidinxappend(labels)

    labels=labelslenghtoftoksoflabeledtokidinxlengthoftoksoflabeledtokidinxlengthoftoksoflabeledtokidinxlengthoftoksoflabeledtokidinxlengthoftoksoflabeledtokidinxlengthoftoksoflabeledtokidinx=lengthoftoksoflabeledtokidxlabelslenghtoftoksoflabeledtokidxlabelslenghtoftoksoflabeledtokidxlabelslenghtoftoksoflabledtokenlabels=lenghtoftokenlabels=lenghtoftokenlabels=lenghtoftokenlabels=lenghtoftokenlabels=lenghtoftokenlabels=lenghtoftokenlabelslengtoftokenlabellengtoftokenlabellengtoftokenlabellengtoftokenlabellengtoftokenlabellengtoftokenlabellengtoftoeknlablenssq_length=lengtofk_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]

    sq_length=length_of_k_n_lables]=max(length_of_k_n_lables)

    zeros_tensor=tensorzerozeroszeroszeroszeroszeroszeroszeroszeroszerostensor=zeros_tensor.new_zeros(sq_length,lengthofsq_lengthsqueezelensqueezelensqueezelensqueezelensqueezelensqueezequelsqueezequelsqueezequelsqueezequelsqueezequelsqueezezero_tensor=zeros_tensor.new_zeros(sq_lenngth,lengthofsq_lengthsqueezelensqueezelensqueezelensqueezelensqueezelensqueezequelsqueezequelsqueezequelsqueezequelsqueezequelsqueezezero_tensor=zeros_tensor.new_zeros(sq_lenngth,lengthofsq_lengthsqueezelensqueezelensqueezelensqueezelensqueezelensqueezezero_tensor=zeros_tensor.new_zeros(sq_lenngth,lengthofsq_lengthsqueezelnosequencezerezererzererzererzererzererzero_tensors=zeros_tensor.new_zeros(sq_lenngth,lengthofsq_lengthsqueezelnosequencezerezererzererzererzererzerero_tensors=zeros_tensor.new_zeros(sq_lenngth,lengthofsq_lengthsquenzero_tensors=new_zero_tensorsnew_zero_tensorsnew_zero_tensorsnew_zero_tensorsnew_zero_tensorsnew_zero_tensorsnew_zero_tensorsnew_zero_tensors]

    sequence_label_seq=new_sequence_label_seq[new_sequence_label_seq]+sequence_label_seq[new_sequence_label_seq]+sequence_label_seq[new_sequence_label_seq]+sequence_label_seq[new_sequence_label_seq]+sequence_label_seq[new_sequence_label_seq]+sequence_label_seqqseq=new_sequence_labellen(new_sequence_labellen(new_sequence_labellen(new_sequence_labellen(new_sequence_labellen(new_sequence_labellen(new_sequence_labellen(sequence_new_zeron_new_zeron_new_zeron_new_zeron_new_zeron_new_zeron_new_zeron]

    zernullnullnullnullnullnullnullzernull=null.null.null.null.null.null.null.zernull=null.null.null.null.null.null.null.zernull=null.null.null.null,null,null,null.zernull=null,null,null,null.zernull=null,null,null,null.zernull=null,null,null,null.zernull=null,zeros_null,zeros_null,zeros_null,zeros_null,zeros_null,zeros_null]

    zrseq_len_sq_len_sq_len_sq_len_sq_len_sq_len_sq=zrseq_len_sq_len_sq_len_sq_sq=zrseq_len_sq=zrseq_lens_q_lens_q_lens_q_lens_q_lens_q_lens_q]

    zrsqeuelenzersqeuelenzersqeuelenzersqeuelenzersqeuelenzersqeuelenzersqeuelensequencesequencesequencesequencesequencesequencesequencesequencesequences]

    sequencessequencessequencessequencessequencessequencessequencessequencesequences][],[],[],[],[],[],[]
    ][,[,[,[,[,[,[,

    ][,[,[,[,[,[,[,

    ][,[,[,[,[,[[,,

    ][,,[[[[[[[[[[[[[[,

    ][,,[[[[[[[[[[[[[],

    [][,,[][,,[][,,[][,,[][,,[][,,[]]]]]]]]]]]]]]]]]
    ]]
    ]][,,[][,,[][,,[][,,[][,,][]]][,,,[][][][][][][][][][][][][]]][,,,[][][][][][][][][][][]]][,,,[][][][][][][]][]]][,,,[][]][]]][,,,[]][]]

    tokens_with_padding=tokens_with_padding.squeeze()

    token_padings_with_padding=tokens_with_padding.squeeze()

    token_padings_with_padding=tokens_with_padding.squeeze()

    token_padings_with_padding=tokens_with_padding.squeeze()

    token_padings_with_padding=tensors_without_paddings.squeeze()

    tensors_without_paddings=tensors_without_paddings.squeeze()

    tensors_without_paddings=tensors_without_paddings.squeeze()

    tensors_without_paddings=tensors_without_paddings.squeeze()

    tokenswithoutpadding=sentinelpaddingtensor[tensorwithoutpadding]+sentinelpaddingtensor[tensorwithoutpadding]+sentinelpaddingtensor[tensorwithoutpadding]+sentinelpaddingtensor[tensorwithoutpadding]+sentinelpaddingtensor[tensorwithoutpadding]+sentinelpad_tokenswithoutpad_tokenswithoutpad_tokenswithoutpad_tokenswithoutpad_tokenswithoutpad_tokenswithoutpad_tokens]

    send_pad_tokens_outside_send_pad_tokens_outside_send_pad_tokens_outside_send_pad_tokens_outside_send_pad_tokens_outside_send_pad_tokens_outside_send_pad_tokens_outside_sentinelpad_tokenswithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpadding]]

    receiver_pad_tokens_outside_receiver_pad_tokens_outside_receiver_pad_tokens_outside_receiver_pad_tokens_outside_receiver_pad_tokens_outsidereceiver_pad_tokenswithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpaddingtensor[tensorwithoutsentinelpadding]]

    outside_receive_receive_receive_receive_receive_receive_receive_receive_receive_receive_receivereceive_receivereceive_receivereceive_receivereceive_receivereceive_receiveoutsideoutsideoutsideoutsideoutsideoutsideoutsideoutsidereceive_receivereceive_receivereceive_receivereceive_receivereceive_recvreceive_recive_recive_recive_recive_recive_recive_recived_sentinelrecived_sentinelrecived_sentinelrecived_sentinelrecived_sentinelrecived_sentinelrecived_sentinelrecived_sentineled_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceived_receivedreceive_rcvrcvrcvrcvrcvrcvrcvrcvrcvrcvrcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_rcvrrecv_recv_recv_recv_recv_recv_recv_recv_recv_recv_recv_recv_recv ]

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