class_docstr: This structure stores a list of bezier curves as a Nx16 torch.Tensor.
  It will support some common methods about bezier shapes
  (`area`, `clip`, `nonempty`, etc),
  and also behaves like a Tensor
  (support indexing, `to(device)`, `.device`, and iteration over all boxes)
  
  Attributes:
      tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
class_docstr_tok: ["This","structure","stores","a","list","of","bezier","curves","as","a","Nx16","torch.Tensor",".","It","will","support","some","common","methods","about","bezier","shapes","(","`","area","`",",","`","clip","`",",","`","nonempty","`",",","etc",")",",","and","also","behaves","like","a","Tensor","(","support","indexing",",","`","to","(","device",")","`",",","`",".device","`",",","and","iteration","over","all","boxes",")","Attributes",":","tensor","(","torch.Tensor",")",":","float","matrix","of","Nx4",".","Each","row","is","(","x1",",","y1",",","x2",",","y2",")","."]
code: def to(self, device: str) -> "Beziers":
      return Beziers(self.tensor.to(device))
  
imports: from typing import Union
  import torch
label_desc: Use image node_id 2 for calling the Beziers obj's underlying member method code with example usage: obj.to(device) and returns: Beziers