class_docstr: T5 style feed-forward conditional layer.
  
  Args:
      d_model (`int`):
          Size of the input hidden states.
      d_ff (`int`):
          Size of the intermediate feed-forward layer.
      dropout_rate (`float`):
          Dropout probability.
      layer_norm_epsilon (`float`):
          A small value used for numerical stability to avoid dividing by zero.
class_docstr_tok: ["T5","style","feed-forward","conditional","layer",".","Args",":","d_model","(","`","int","`",")",":","Size","of","the","input","hidden","states",".","d_ff","(","`","int","`",")",":","Size","of","the","intermediate","feed-forward","layer",".","dropout_rate","(","`","float","`",")",":","Dropout","probability",".","layer_norm_epsilon","(","`","float","`",")",":","A","small","value","used","for","numerical","stability","to","avoid","dividing","by","zero","."]
code: def forward(
      self,
      hidden_states: torch.FloatTensor,
      conditioning_emb: Optional[torch.FloatTensor] = None,
  ) -> torch.FloatTensor:
      forwarded_states = self.layer_norm(hidden_states)
      if conditioning_emb is not None:
          forwarded_states = self.film(
              forwarded_states, conditioning_emb
          )
  
      forwarded_states = self.DenseReluDense(forwarded_states)
      hidden_states = hidden_states + self.dropout(forwarded_states)
      return hidden_states
  
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from .attention_processor import Attention
  from .embeddings import get_timestep_embedding
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 2 for calling the T5LayerFFCond obj's underlying member method code with example usage: obj.forward(hidden_states, conditioning_emb) and returns: hidden_states