r""" Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this option is enabled, you

The [`Transformer1DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pix

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression lo

Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch

Args: pcd_prev: Tensor, (B, 3, N_prev) feat_cond: Tensor, (B, dim_feat, N_prev) K_prev: Tensor, (B, 128,

Args: points: Tensor, (b, num_p0, 3) feat_cond: Tensor, (b, dim_feat) dinov2: 325x768 # partial_coarse: T

Args: x: (B, out_dim, n)

Args: xyz: Tensor, (B, 3, N) points: Tensor, (B, f, N) Returns: new_xyz: Tensor, (B, 3, npoint)

Args: xyz1: Tensor, (B, 3, N) xyz2: Tensor, (B, 3, M) points1: Tensor, (B, in_channel, N) poi

Args: xyz: Tensor, (B, 3, N) points: Tensor, (B, f, N) Returns: new_xyz: Tensor, (B, 3, npoint)

feed forward of transformer Args: x: Tensor of features, (B, in_channel, n) pos: Tensor of positions, (B, 3, n)

Args: pos: (B, 3, N) key: (B, in_channel, N) query: (B, in_channel, N) include_self: boolean

Args: pcd_prev: Tensor, (B, 3, N_prev) feat_global: Tensor, (B, dim_feat, 1) K_prev: Tensor, (B, 128, N_p

Args: pcd_prev: Tensor, (B, 3, N_prev) feat_global: Tensor, (B, dim_feat, 1) K_prev: Tensor, (B, 128, N_p

r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Paramet

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, n

r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor

r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of features to group idx : torch.Tens

r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features feat

r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features

Args pcd: (b, 16384, 3) returns new_pcd: (b, n_points, 3)