class_docstr: Computes the hyperbolic inner product between elements of a rank 2 Tensor:
  $$
      \langle \x, \x \rangle = \x \,
      \begin{pmatrix}
          \I_{d \times d} & 0 \\
          0              & -1
      \end{pmatrix} \, \x^\top.
  $$
  
  **Mode**: single.
  
  **Input**
  
  - Tensor of shape `(n_nodes, n_features)`;
  
  **Output**
  
  - Tensor of shape `(n_nodes, n_nodes)`.
  
  :param activation: activation function;
code: def call(self, inputs):
      F = tf.shape(inputs)[-1]
      minkowski_prod_mat = np.eye(F)
      minkowski_prod_mat[-1, -1] = -1.0
      minkowski_prod_mat = K.constant(minkowski_prod_mat)
      output = K.dot(inputs, minkowski_prod_mat)
      output = K.dot(output, K.transpose(inputs))
      output = K.clip(output, -10e9, -1.0)
  
      if self.activation is not None:
          output = self.activation(output)
  
      return output
  
imports: import numpy
  import tensorflow
  from tensorflow.keras import activations
  from tensorflow.keras import backend
  from tensorflow.keras import constraints, initializers, regularizers
  from tensorflow.keras.layers import Layer
  from tensorflow.python.framework import smart_cond
  from spektral.layers import ops
label_desc: Use image node_id 3 for calling the MinkowskiProduct obj's underlying member method code with example usage: obj.call(inputs) and returns: output