class_docstr: Apply average pooling over the nodes in a graph.
  
  .. math::
      r^{(i)} = \frac{1}{N_i}\sum_{k=1}^{N_i} x^{(i)}_k
  
  Notes
  -----
      Input: Could be one graph, or a batch of graphs. If using a batch of graphs,
      make sure nodes in all graphs have the same feature size, and concatenate
      nodes' feature together as the input.
  
  Examples
  --------
  The following example uses PyTorch backend.
  
  >>> import dgl
  >>> import torch as th
  >>> from dgl.nn import AvgPooling
  >>>
  >>> g1 = dgl.rand_graph(3, 4)  # g1 is a random graph with 3 nodes and 4 edges
  >>> g1_node_feats = th.rand(3, 5)  # feature size is 5
  >>> g1_node_feats
  tensor([[0.8948, 0.0699, 0.9137, 0.7567, 0.3637],
          [0.8137, 0.8938, 0.8377, 0.4249, 0.6118],
          [0.5197, 0.9030, 0.6825, 0.5725, 0.4755]])
  >>>
  >>> g2 = dgl.rand_graph(4, 6)  # g2 is a random graph with 4 nodes and 6 edges
  >>> g2_node_feats = th.rand(4, 5)  # feature size is 5
  >>> g2_node_feats
  tensor([[0.2053, 0.2426, 0.4111, 0.9028, 0.5658],
          [0.5278, 0.6365, 0.9990, 0.2351, 0.8945],
          [0.3134, 0.0580, 0.4349, 0.7949, 0.3891],
          [0.0142, 0.2709, 0.3330, 0.8521, 0.6925]])
  >>>
  >>> avgpool = AvgPooling()  # create an average pooling layer
  
  Case 1: Input single graph
  
  >>> avgpool(g1, g1_node_feats)
  tensor([[0.7427, 0.6222, 0.8113, 0.5847, 0.4837]])
  
  Case 2: Input a batch of graphs
  
  Build a batch of DGL graphs and concatenate all graphs' note features into one tensor.
  
  >>> batch_g = dgl.batch([g1, g2])
  >>> batch_f = th.cat([g1_node_feats, g2_node_feats])
  >>>
  >>> avgpool(batch_g, batch_f)
  tensor([[0.7427, 0.6222, 0.8113, 0.5847, 0.4837],
          [0.2652, 0.3020, 0.5445, 0.6962, 0.6355]])
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code: def __init__(self):
      super(AvgPooling, self).__init__()
  
imports: import numpy
  import torch
  import torch.nn
  from ...backend import pytorch
  from ...base import dgl_warning
  from ...readout import broadcast_nodes, max_nodes, mean_nodes, softmax_nodes, sum_nodes, topk_nodes
label_desc: Use image node_id 1 to create a new AvgPooling object from inherited base classes: nn with example: obj = AvgPooling()