class_docstr: Row-wise correlation pyramid.
  
  Create a row-wise correlation pyramid with ``num_levels`` level from the outputs of the feature encoder,
  this correlation pyramid will later be used as index to create correlation features using CorrBlock1d.
class_docstr_tok: ["Row-wise","correlation","pyramid",".","Create","a","row-wise","correlation","pyramid","with","``","num_levels","``","level","from","the","outputs","of","the","feature","encoder",",","this","correlation","pyramid","will","later","be","used","as","index","to","create","correlation","features","using","CorrBlock1d","."]
code: def __init__(self, num_levels: int = 4):
      super().__init__()
      self.num_levels = num_levels
  
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 CorrPyramid1d object from inherited base classes: nn with example: obj = CorrPyramid1d(num_levels)