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).
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docstr: Returns:
      Beziers: Create a new :class:`Beziers` by indexing.
docstr_tok: ["Returns",":","Beziers",":","Create","a","new",":","class",":","`","Beziers","`","by","indexing","."]
code: def __getitem__(
      self, item: Union[int, slice, torch.BoolTensor]
  ) -> "Beziers":
      """
      Returns:
          Beziers: Create a new :class:`Beziers` by indexing.
      """
      if isinstance(item, int):
          return Beziers(self.tensor[item].view(1, -1))
      b = self.tensor[item]
      assert (
          b.dim() == 2
      ), "Indexing on Boxes with {} failed to return a matrix!".format(
          item
      )
      return Beziers(b)
  
imports: from typing import Union
  import torch
label_desc: Use image node_id 3 for calling the Beziers obj's underlying member method code with example usage: obj.__getitem__(item) and returns: Beziers, Beziers