code: def baseline_step(self, prev, inputs):
      if not self.use_baselines:
          return dict(), None, None
  
      # Prevent gradients flowing back from baseline loss to
      # inference net by detaching from graph here.
      rnn_input = torch.cat(
          (
              inputs["bl_embed"],
              prev.z_where.detach(),
              prev.z_what.detach(),
              prev.z_pres.detach(),
          ),
          1,
      )
      bl_h, bl_c = self.bl_rnn(rnn_input, (prev.bl_h, prev.bl_c))
      bl_value = self.bl_predict(bl_h)
  
      # Zero out values for finished data points. This avoids adding
      # superfluous terms to the loss.
      if self.use_masking:
          bl_value = bl_value * prev.z_pres
  
      # The value that the baseline net is estimating can be very
      # large. An option to scale the nets output is provided
      # to make it easier for the net to output values of this
      # scale.
      if self.baseline_scalar is not None:
          bl_value = bl_value * self.baseline_scalar
  
      infer_dict = dict(
          baseline=dict(baseline_value=bl_value.squeeze(-1))
      )
      return infer_dict, bl_h, bl_c
  
imports: from collections import namedtuple
  import torch
  import torch.nn
  import torch.nn.functional
  from modules import MLP, Decoder, Encoder, Identity, Predict
  import pyro
  import pyro.distributions
label_desc: Use image node_id 7 for calling the AIR obj's underlying member method code with example usage: obj.baseline_step(prev, inputs) and returns: infer_dict,  bl_h,  bl_c, dict,  None,  None