forward pass of backprojection for 2d features onto 3d points :param label: image features (shape: (num_input_channels, proj_image_d

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

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

r""" Propagate features from xyz1 to xyz2. Parameters ---------- xyz2 : torch.Tensor (B, N2, 3) tensor of the xyz

r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the masked l

r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Seq

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the masked l

Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).

r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5Model >>> tokenizer = T5Tokeni

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

r""" Returns: Example: ```python >>> from transformers import T5Tokenizer, T5EncoderModel >>> tokenizer = T

`input_ids_shape` is expected to be [bsz x seqlen].

Input shape: Batch x Time x Channel

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence toke

r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence toke