class_docstr: A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
  sample shaped output.
  
  This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
  for all models (such as downloading or saving).
code: def disable_forward_chunking(self) -> None:
      def fn_recursive_feed_forward(
          module: torch.nn.Module, chunk_size: int, dim: int
      ):
          if hasattr(module, "set_chunk_feed_forward"):
              module.set_chunk_feed_forward(
                  chunk_size=chunk_size, dim=dim
              )
  
          for child in module.children():
              fn_recursive_feed_forward(child, chunk_size, dim)
  
      for module in self.children():
          fn_recursive_feed_forward(module, None, 0)
  
imports: from typing import Any, Dict, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..loaders import UNet2DConditionLoadersMixin
  from ..utils import logging
  from .attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
  from .transformer_temporal import TransformerTemporalModel
  from .unet_2d_blocks import UNetMidBlock2DCrossAttn
  from .unet_2d_condition import UNet2DConditionModel
  from .unet_3d_blocks import CrossAttnDownBlockMotion, CrossAttnUpBlockMotion, DownBlockMotion, UNetMidBlockCrossAttnMotion, UpBlockMotion, get_down_block, get_up_block
  from .unet_3d_condition import UNet3DConditionOutput
label_desc: Use image node_id 9 for calling the UNetMotionModel obj's underlying member method code with example usage: obj.disable_forward_chunking() without return types