class_docstr: T5 style FiLM Layer.
  
  Args:
      in_features (`int`):
          Number of input features.
      out_features (`int`):
          Number of output features.
class_docstr_tok: ["T5","style","FiLM","Layer",".","Args",":","in_features","(","`","int","`",")",":","Number","of","input","features",".","out_features","(","`","int","`",")",":","Number","of","output","features","."]
code: def forward(
      self, x: torch.FloatTensor, conditioning_emb: torch.FloatTensor
  ) -> torch.FloatTensor:
      emb = self.scale_bias(conditioning_emb)
      scale, shift = torch.chunk(emb, 2, -1)
      x = x * (1 + scale) + shift
      return x
  
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 T5FiLMLayer obj's underlying member method code with example usage: obj.forward(x, conditioning_emb) and returns: x