def forward()

in agents/ed_pcq.py [0:0]

• 20 lines of code
• 9 McCabe index (conditional complexity)

    def forward(self, *inputs, **kwargs):
        flat_inputs = torch.cat(inputs, dim=-1)

        state_dim = inputs[0].shape[-1]

        dim = len(flat_inputs.shape)
        # repeat h to make amenable to parallelization
        # if dim = 3, then we probably already did this somewhere else
        # (e.g. bootstrapping in training optimization)
        if dim < 3:
            flat_inputs = flat_inputs.unsqueeze(0)
            if dim == 1:
                flat_inputs = flat_inputs.unsqueeze(0)
            flat_inputs = flat_inputs.repeat(self.ensemble_size, 1, 1)

        # input normalization
        h = flat_inputs

        # standard feedforward network
        for _, fc in enumerate(self.fcs):
            h = fc(h)
            h = self.hidden_activation(h)
            if hasattr(self, 'layer_norm') and (self.layer_norm is not None):
                h = self.layer_norm(h)
        preactivation = self.last_fc(h)
        output = self.output_activation(preactivation)

        # if original dim was 1D, squeeze the extra created layer
        if dim == 1:
            output = output.squeeze(1)

        # output is (ensemble_size, batch_size, output_size)
        return output