ISubGVQA.models.att_pooling
===========================

.. py:module:: ISubGVQA.models.att_pooling


Classes
-------

.. autoapisummary::

   ISubGVQA.models.att_pooling.GlobalAttention


Module Contents
---------------

.. py:class:: GlobalAttention(num_node_features, num_out_features)

   Bases: :py:obj:`torch.nn.Module`


   GlobalAttention is a neural network module that applies attention mechanisms to graph data.
   Args:
       num_node_features (int): The number of input features per node.
       num_out_features (int): The number of output features.
   Methods:
       reset_parameters():
           Resets the parameters of the neural network layers.
       forward(x, u, batch, size=None, return_mask=False, node_mask=None):
           Forward pass of the GlobalAttention module.
           Args:
               x (Tensor): Node feature matrix with shape [num_nodes, num_node_features].
               u (Tensor): Global feature matrix with shape [batch_size, num_out_features].
               batch (Tensor): Batch vector which assigns each node to a specific example in the batch.
               size (int, optional): The number of examples in the batch. If None, it is inferred from the batch vector.
               return_mask (bool, optional): If True, returns the attention mask along with the output.
               node_mask (Tensor, optional): Mask to apply on the node features.
           Returns:
               Tensor: The output feature matrix with shape [batch_size, num_out_features].
               Tensor (optional): The attention mask with shape [num_nodes, 1] if return_mask is True.
       __repr__():
           Returns a string representation of the GlobalAttention module.


   .. py:attribute:: gate_nn


   .. py:attribute:: node_nn


   .. py:attribute:: ques_nn


   .. py:method:: reset_parameters()


   .. py:method:: forward(x, u, batch, size=None, return_mask=False, node_mask=None)


   .. py:method:: __repr__()