class_docstr: A sequential module that passes timestep embeddings to the children that
  support it as an extra input.
code: def forward(self, x, emb, encoder_out=None):
      for layer in self:
          if isinstance(layer, TimestepBlock):
              x = layer(x, emb)
          elif isinstance(layer, AttentionBlock):
              x = layer(x, encoder_out)
          else:
              x = layer(x)
      return x
  
imports: import math
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from .fp16_util import convert_module_to_f16, convert_module_to_f32
  from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
label_desc: Use image node_id 1 for calling the TimestepEmbedSequential obj's underlying member method code with example usage: obj.forward(x, emb, encoder_out) and returns: x