class_docstr: MNIST classifier used in the experiments for Counterfactual with Reinforcement Learning. The model consists of two
  convolutional layers having 64 and 32 channels and a kernel size of 2 with ReLU nonlinearities, followed by
  maxpooling of size 2 and dropout of 0.3. The convolutional block is followed by a fully connected layer of 256 with
  ReLU nonlinearity, and finally a fully connected layer is used to predict the class logits (10 in MNIST case).
docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  training
      Training flag.
  **kwargs
      Other arguments. Not used.
  
  Returns
  -------
  Classification logits.
docstr_tok: ["Forward","pass",".","Parameters","--","--","--","--","--","x","Input","tensor",".","training","Training","flag",".","*","*","kwargs","Other","arguments",".","Not","used",".","Returns","--","--","--","-","Classification","logits","."]
code: def call(
      self, x: tf.Tensor, training: bool = True, **kwargs
  ) -> tf.Tensor:
      """
      Forward pass.
  
      Parameters
      ----------
      x
          Input tensor.
      training
          Training flag.
      **kwargs
          Other arguments. Not used.
  
      Returns
      -------
      Classification logits.
      """
      x = self.dropout1(self.maxpool1(self.conv1(x)), training=training)
      x = self.dropout2(self.maxpool2(self.conv2(x)), training=training)
      x = self.fc2(self.fc1(self.flatten(x)))
      return x
  
imports: import tensorflow
  import tensorflow.keras
  from typing import List
label_desc: Use image node_id 2 for calling the MNISTClassifier obj's underlying member method code with example usage: obj.call(x, training) and returns: x