code: def __init__(self, num_channels, max_positions=10000, endpoint=False):
      super().__init__()
      self.num_channels = num_channels
      self.max_positions = max_positions
      self.endpoint = endpoint
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 1 to create a new PositionalEmbedding object from inherited base classes: torch.nn with example: obj = PositionalEmbedding(num_channels, max_positions, endpoint)