code: def forward_with_cond_scale(
      self, *args, text_embed=None, cond_scale=1.0, **kwargs
  ):
      logits = self.forward(*args, text_embed=text_embed, **kwargs)
      if cond_scale == 1.0:
          return logits
      null_logits = self.forward(
          *args, text_embed=torch.zeros_like(text_embed), **kwargs
      )
      return null_logits + (logits - null_logits) * cond_scale
  
imports: import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_mul
  from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes
  from nemo.collections.multimodal.parts.utils import randn_like
label_desc: Use image node_id 4 for calling the PrecondModel obj's underlying member method code with example usage: obj.forward_with_cond_scale() and returns: unknown, logits