code: def _raft_stereo(
      *,
      weights: Optional[WeightsEnum],
      progress: bool,
      shared_encoder_weight: bool,
      # Feature encoder
      feature_encoder_layers: Tuple[int, int, int, int, int],
      feature_encoder_strides: Tuple[int, int, int, int],
      feature_encoder_block: Callable[..., nn.Module],
      # Context encoder
      context_encoder_layers: Tuple[int, int, int, int, int],
      context_encoder_strides: Tuple[int, int, int, int],
      # if the `out_with_blocks` param of the context_encoder is True, then
      # the particular output on that level position will have additional `context_encoder_block` layer
      context_encoder_out_with_blocks: List[bool],
      context_encoder_block: Callable[..., nn.Module],
      # Correlation block
      corr_num_levels: int,
      corr_radius: int,
      # Motion encoder
      motion_encoder_corr_layers: Tuple[int, int],
      motion_encoder_flow_layers: Tuple[int, int],
      motion_encoder_out_channels: int,
      # Update block
      update_block_hidden_dims: List[int],
      # Flow Head
      flow_head_hidden_size: int,
      # Mask predictor
      mask_predictor_hidden_size: int,
      use_mask_predictor: bool,
      slow_fast: bool,
      **kwargs,
  ):
      if len(context_encoder_out_with_blocks) != len(
          update_block_hidden_dims
      ):
          raise ValueError(
              "Length of context_encoder_out_with_blocks and update_block_hidden_dims must be the same"
              + "because both of them represent the number of GRUs level"
          )
      if shared_encoder_weight:
          if (
              feature_encoder_layers[:-1] != context_encoder_layers[:-1]
              or feature_encoder_strides != context_encoder_strides
          ):
              raise ValueError(
                  "If shared_encoder_weight is True, then the feature_encoder_layers[:-1]"
                  + " and feature_encoder_strides must be the same with context_encoder_layers[:-1] and context_encoder_strides!"
              )
  
          base_encoder = kwargs.pop(
              "base_encoder", None
          ) or BaseEncoder(
              block=context_encoder_block,
              layers=context_encoder_layers[:-1],
              strides=context_encoder_strides,
              norm_layer=nn.BatchNorm2d,
          )
          feature_base_encoder = base_encoder
          context_base_encoder = base_encoder
      else:
          feature_base_encoder = BaseEncoder(
              block=feature_encoder_block,
              layers=feature_encoder_layers[:-1],
              strides=feature_encoder_strides,
              norm_layer=nn.InstanceNorm2d,
          )
          context_base_encoder = BaseEncoder(
              block=context_encoder_block,
              layers=context_encoder_layers[:-1],
              strides=context_encoder_strides,
              norm_layer=nn.BatchNorm2d,
          )
      feature_encoder = kwargs.pop(
          "feature_encoder", None
      ) or FeatureEncoder(
          feature_base_encoder,
          output_dim=feature_encoder_layers[-1],
          shared_base=shared_encoder_weight,
          block=feature_encoder_block,
      )
      context_encoder = kwargs.pop(
          "context_encoder", None
      ) or MultiLevelContextEncoder(
          context_base_encoder,
          out_with_blocks=context_encoder_out_with_blocks,
          output_dim=context_encoder_layers[-1],
          block=context_encoder_block,
      )
  
      feature_downsampling_ratio = (
          feature_encoder.base_downsampling_ratio
      )
  
      corr_pyramid = kwargs.pop("corr_pyramid", None) or CorrPyramid1d(
          num_levels=corr_num_levels
      )
      corr_block = kwargs.pop("corr_block", None) or CorrBlock1d(
          num_levels=corr_num_levels, radius=corr_radius
      )
  
      motion_encoder = kwargs.pop(
          "motion_encoder", None
      ) or MotionEncoder(
          in_channels_corr=corr_block.out_channels,
          corr_layers=motion_encoder_corr_layers,
          flow_layers=motion_encoder_flow_layers,
          out_channels=motion_encoder_out_channels,
      )
      update_block = kwargs.pop(
          "update_block", None
      ) or MultiLevelUpdateBlock(
          motion_encoder=motion_encoder,
          hidden_dims=update_block_hidden_dims,
      )
  
      # We use the largest scale hidden_dims of update_block to get the predicted disparity
      disparity_head = kwargs.pop("disparity_head", None) or FlowHead(
          in_channels=update_block_hidden_dims[0],
          hidden_size=flow_head_hidden_size,
      )
  
      mask_predictor = kwargs.pop("mask_predictor", None)
      if use_mask_predictor:
          mask_predictor = MaskPredictor(
              in_channels=update_block.hidden_dims[0],
              hidden_size=mask_predictor_hidden_size,
              out_channels=9
              * feature_downsampling_ratio
              * feature_downsampling_ratio,
          )
      else:
          mask_predictor = None
  
      model = RaftStereo(
          feature_encoder=feature_encoder,
          context_encoder=context_encoder,
          corr_pyramid=corr_pyramid,
          corr_block=corr_block,
          update_block=update_block,
          disparity_head=disparity_head,
          mask_predictor=mask_predictor,
          slow_fast=slow_fast,
          **kwargs,  # not really needed, all params should be consumed by now
      )
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
imports: from functools import partial
  from typing import Callable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.models.optical_flow.raft
  from torch import Tensor
  from torchvision.models._api import register_model, Weights, WeightsEnum
  from torchvision.models._utils import handle_legacy_interface
  from torchvision.models.optical_flow._utils import grid_sample, make_coords_grid, upsample_flow
  from torchvision.models.optical_flow.raft import FlowHead, MotionEncoder, ResidualBlock
  from torchvision.ops import Conv2dNormActivation
  from torchvision.prototype.transforms._presets import StereoMatching
  from torchvision.utils import _log_api_usage_once
label_desc: Use image node_id 1 for calling a global function with example usage: _raft_stereo() and returns: model