class_docstr: A Prior Transformer model.
  
  Parameters:
      num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
      attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
      num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.
      embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states`
      num_embeddings (`int`, *optional*, defaults to 77):
          The number of embeddings of the model input `hidden_states`
      additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the
          projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
          additional_embeddings`.
      dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
      time_embed_act_fn (`str`, *optional*, defaults to 'silu'):
          The activation function to use to create timestep embeddings.
      norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before
          passing to Transformer blocks. Set it to `None` if normalization is not needed.
      embedding_proj_norm_type (`str`, *optional*, defaults to None):
          The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not
          needed.
      encoder_hid_proj_type (`str`, *optional*, defaults to `linear`):
          The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if
          `encoder_hidden_states` is `None`.
      added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model.
          Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot
          product between the text embedding and image embedding as proposed in the unclip paper
          https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.
      time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.
          If None, will be set to `num_attention_heads * attention_head_dim`
      embedding_proj_dim (`int`, *optional*, default to None):
          The dimension of `proj_embedding`. If None, will be set to `embedding_dim`.
      clip_embed_dim (`int`, *optional*, default to None):
          The dimension of the output. If None, will be set to `embedding_dim`.
code: def post_process_latents(self, prior_latents):
      prior_latents = (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
  from ..utils import BaseOutput
  from .attention import BasicTransformerBlock
  from .attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 6 for calling the PriorTransformer obj's underlying member method code with example usage: obj.post_process_latents(prior_latents) and returns: prior_latents