class_docstr: The row-wise correlation block.
  
  Use indexes from correlation pyramid to create correlation features.
  The "indexing" of a given centroid pixel x' is done by concatenating its surrounding row neighbours
  within radius
class_docstr_tok: ["The","row-wise","correlation","block",".","Use","indexes","from","correlation","pyramid","to","create","correlation","features",".","The","``","indexing","''","of","a","given","centroid","pixel","x","'","is","done","by","concatenating","its","surrounding","row","neighbours","within","radius"]
code: def __init__(self, *, num_levels: int = 4, radius: int = 4):
      super().__init__()
      self.radius = radius
      self.out_channels = num_levels * (2 * radius + 1)
  
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 to create a new CorrBlock1d object from inherited base classes: nn with example: obj = CorrBlock1d()