code: def get_input(
      self,
      batch,
      k,
      return_first_stage_outputs=False,
      force_c_encode=False,
      cond_key=None,
      return_original_cond=False,
      bs=None,
      uncond=0.05,
  ):
      x = batch[k]
      if bs is not None:
          x = x[:bs]
  
      encoder_posterior = self.encode_first_stage(x)
      z = self.get_first_stage_encoding(encoder_posterior).detach()
      cond_key = cond_key or self.cond_stage_key
      xc = batch[cond_key]
      if bs is not None:
          xc["c_crossattn"] = xc["c_crossattn"][:bs]
          xc["c_concat"] = xc["c_concat"][:bs]
      cond = {}
  
      # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
      random = torch.rand(x.size(0), device=x.device)
      prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
      input_mask = 1 - rearrange(
          (random >= uncond).float() * (random < 3 * uncond).float(),
          "n -> n 1 1 1",
      )
  
      null_prompt = self.get_learned_conditioning([""])
      cond["c_crossattn"] = torch.where(
          prompt_mask,
          null_prompt,
          self.get_learned_conditioning(xc["c_crossattn"]).detach(),
      )
      cond["c_concat"] = (
          input_mask
          * self.encode_first_stage((xc["c_concat"].to(x.device)))
          .mode()
          .detach()
      )
  
      out = [z, cond]
      if return_first_stage_outputs:
          xrec = self.decode_first_stage(z)
          out.extend([x, xrec])
      if return_original_cond:
          out.append(xc)
      return out
  
file_path: NeMo/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/ddpm_edit.py
imports: import torch
  from einops import rearrange
  from nemo.collections.multimodal.data.instruct_pix2pix.edit_dataset import EditDataset
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion, MegatronLatentDiffusion
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the LatentDiffusionEdit obj's underlying member method code with example usage: obj.get_input(batch, k, return_first_stage_outputs, force_c_encode, cond_key, return_original_cond, bs, uncond) and returns: out