class_docstr: The half UNet model with attention and timestep embedding.
  
  For usage, see UNet.
class_docstr_tok: ["The","half","UNet","model","with","attention","and","timestep","embedding",".","For","usage",",","see","UNet","."]
docstr: Apply the model to an input batch.
  
  :param x: an [N x C x ...] Tensor of inputs.
  :param timesteps: a 1-D batch of timesteps.
  :return: an [N x K] Tensor of outputs.
docstr_tok: ["Apply","the","model","to","an","input","batch",".",":","param","x",":","an","[","N","x","C","x","...","]","Tensor","of","inputs",".",":","param","timesteps",":","a","1-D","batch","of","timesteps",".",":","return",":","an","[","N","x","K","]","Tensor","of","outputs","."]
code: def forward(self, x, timesteps):
      """
      Apply the model to an input batch.
  
      :param x: an [N x C x ...] Tensor of inputs.
      :param timesteps: a 1-D batch of timesteps.
      :return: an [N x K] Tensor of outputs.
      """
      emb = self.time_embed(
          timestep_embedding(timesteps, self.model_channels)
      )
  
      results = []
      # h = x.type(self.dtype)
      h = paddle.cast(x, self.dtype)
      for module in self.input_blocks:
          h = module(h, emb)
          if self.pool.startswith("spatial"):
              # results.append(h.type(x.dtype).mean(axis=(2, 3)))
              results.append(paddle.cast(h, x.dtype).mean(axis=(2, 3)))
      h = self.middle_block(h, emb)
      if self.pool.startswith("spatial"):
          results.append(paddle.cast(h, x.dtype).mean(axis=(2, 3)))
          h = paddle.concat(results, axis=-1)
          return self.out(h)
      else:
          # h = h.type(x.dtype)
          h = paddle.cast(h, x.dtype)
          return self.out(h)
  
imports: import math
  from abc import abstractmethod
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from .nn import avg_pool_nd
  from .nn import checkpoint
  from .nn import conv_nd
  from .nn import linear
  from .nn import normalization
  from .nn import SiLU
  from .nn import timestep_embedding
  from .nn import zero_module
label_desc: Use image node_id 2 for calling the EncoderUNetModel obj's underlying member method code with example usage: obj.forward(x, timesteps) and returns: self, self