class_docstr: This Layer computes the Readout function for Deep Graph Infomax (https://arxiv.org/pdf/1809.10341.pdf).
class_docstr_tok: ["This","Layer","computes","the","Readout","function","for","Deep","Graph","Infomax","(","https",":","\/\/arxiv.org\/pdf\/1809.10341.pdf",")","."]
docstr: Applies the layer to the inputs.
  
  Args:
      node_feats: a tensor containing the batch node features from the base model. This has shape `(1, N, F)`
          for full batch methods and shape `(B, F)` for sampled node methods. Where `N` is the number of nodes
          in the graph, `F` is the feature dimension, and `B` is the batch size.
  
  Returns:
      a Tensor with shape `(1, F)` for full batch methods and shape `(F,)` for sampled node methods.
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code: def call(self, node_feats):
      """
      Applies the layer to the inputs.
  
      Args:
          node_feats: a tensor containing the batch node features from the base model. This has shape `(1, N, F)`
              for full batch methods and shape `(B, F)` for sampled node methods. Where `N` is the number of nodes
              in the graph, `F` is the feature dimension, and `B` is the batch size.
  
      Returns:
          a Tensor with shape `(1, F)` for full batch methods and shape `(F,)` for sampled node methods.
      """
  
      summary = tf.reduce_mean(node_feats, axis=-2)
      summary = tf.math.sigmoid(summary)
  
      return summary
  
imports: from .None import GCN, GAT, APPNP, PPNP, GraphSAGE, DirectedGraphSAGE
  from .misc import deprecated_model_function
  from ..mapper import CorruptedGenerator
  from tensorflow.keras.layers import Input, Lambda, Layer, GlobalAveragePooling1D
  import tensorflow
  from tensorflow.keras import backend
  import warnings
  import numpy
label_desc: Use image node_id 3 for calling the DGIReadout obj's underlying member method code with example usage: obj.call(node_feats) and returns: summary