class_docstr: Apply max pooling over the nodes in the graph.
  
  .. math::
      r^{(i)} = \max_{k=1}^{N_i}\left( x^{(i)}_k \right)
docstr: Compute max pooling.
  
  Parameters
  ----------
  graph : GraphData
      The graph data.
  feat : str
      The feature field name.
  
  Returns
  -------
  torch.Tensor
      The output feature.
docstr_tok: ["Compute","max","pooling",".","Parameters","--","--","--","--","--","graph",":","GraphData","The","graph","data",".","feat",":","str","The","feature","field","name",".","Returns","--","--","--","-","torch.Tensor","The","output","feature","."]
code: def forward(self, graph, feat):
      r"""Compute max pooling.
  
      Parameters
      ----------
      graph : GraphData
          The graph data.
      feat : str
          The feature field name.
  
      Returns
      -------
      torch.Tensor
          The output feature.
      """
      graph_list = from_batch(graph)
      output_feat = []
      for g in graph_list:
          feat_tensor = g.node_features[feat]
          if self.linear is not None:
              feat_tensor = self.linear(feat_tensor)
  
          output_feat.append(torch.max(feat_tensor, dim=0)[0])
  
      output_feat = torch.stack(output_feat, 0)
  
      return output_feat
  
imports: import torch
  import torch.nn
  from .....data.data import from_batch
  from ..base import PoolingBase
label_desc: Use image node_id 2 for calling the MaxPooling obj's underlying member method code with example usage: obj.forward(graph, feat) and returns: output_feat