code: def forward(self, inputs, conditions):
      batch_size = inputs.size()[0]
      outputs = inputs.view(batch_size, self.in_channels, -1)
      for layer in self.pre_cond_layers:
          outputs = layer(outputs)
      outputs = self.cond_block(outputs, conditions)
      for layer in self.post_cond_layers:
          outputs = layer(outputs)
  
      return outputs
  
imports: import numpy
  from torch import nn
label_desc: Use image node_id 2 for calling the ConditionalDiscriminator obj's underlying member method code with example usage: obj.forward(inputs, conditions) and returns: outputs