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: Convert the torso of the model to float16.
docstr_tok: ["Convert","the","torso","of","the","model","to","float16","."]
code: def convert_to_fp16(self):
      """
      Convert the torso of the model to float16.
      """
      self.input_blocks.apply(convert_module_to_f16)
      self.middle_block.apply(convert_module_to_f16)
  
imports: from abc import abstractmethod
  from functools import partial
  import math
  from typing import Iterable
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ldm.modules.diffusionmodules.util import checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding
  from ldm.modules.attention import SpatialTransformer
label_desc: Use image node_id 2 for calling the EncoderUNetModel obj's underlying member method code with example usage: obj.convert_to_fp16() without return types