class_docstr: An implementation of the Deep Neural Network for Temporal Set Prediction.
  For details see: `"Predicting Temporal Sets with Deep Neural Networks" <https://dl.acm.org/doi/abs/10.1145/3394486.3403152>`_
  
  Args:
      items_total (int): Total number of items in the sets. Cardinality of the union.
      item_embedding_dim (int): Item embedding dimensions.
      n_heads (int): Number of attention heads.
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code: def __init__(
      self, items_total: int, item_embedding_dim: int, n_heads: int
  ):
      super(DNNTSP, self).__init__()
      self.item_embedding = nn.Embedding(
          items_total, item_embedding_dim
      )
      self.item_embedding_dim = item_embedding_dim
      self.items_total = items_total
  
      self.stacked_gcn = WeightedGCNBlock(
          item_embedding_dim, [item_embedding_dim], item_embedding_dim
      )
  
      self.masked_self_attention = MaskedSelfAttention(
          input_dim=item_embedding_dim,
          output_dim=item_embedding_dim,
          n_heads=n_heads,
      )
  
      self.aggregate_nodes_temporal_feature = (
          AggregateTemporalNodeFeatures(
              item_embed_dim=item_embedding_dim
          )
      )
  
      self.global_gated_updater = GlobalGatedUpdater(
          items_total=items_total, item_embedding=self.item_embedding
      )
  
imports: import torch
  import numpy
  import torch.nn
  from typing import List
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 1 to create a new DNNTSP object from inherited base classes: nn with example: obj = DNNTSP(items_total, item_embedding_dim, n_heads)