class_docstr: This Layer computes the Discriminator function for Deep Graph Infomax (https://arxiv.org/pdf/1809.10341.pdf).
  
  .. seealso:: :class:`.DeepGraphInfomax` uses this layer.
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docstr: Applies the layer to the inputs.
  
  Args:
      inputs: a list or tuple of tensors with shapes ``[(1, N, F), (1, F)]`` for full batch methods and shapes
          ``[(B, F), (F,)]`` for sampled node methods, containing the node features and a summary feature vector.
          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, N)`` for full batch methods and shape ``(B,)`` for sampled node methods.
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code: def call(self, inputs):
      """
      Applies the layer to the inputs.
  
      Args:
          inputs: a list or tuple of tensors with shapes ``[(1, N, F), (1, F)]`` for full batch methods and shapes
              ``[(B, F), (F,)]`` for sampled node methods, containing the node features and a summary feature vector.
              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, N)`` for full batch methods and shape ``(B,)`` for sampled node methods.
      """
  
      features, summary = inputs
  
      score = tf.linalg.matvec(
          features,
          tf.linalg.matvec(self.kernel, summary),
      )
  
      return score
  
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 DGIDiscriminator obj's underlying member method code with example usage: obj.call(inputs) and returns: score