code: def parametrize_knots(knot_spacings: torch.Tensor) -> torch.Tensor:
      # the last entry is 1 and can be omitted
      knot_positions = torch.cumsum(knot_spacings, dim=-1)[..., :-1]
  
      # the first knot pos is 0
      knot_positions = torch.cat(
          [torch.zeros_like(knot_positions[..., 0:1]), knot_positions],
          dim=-1,
      )
  
      return knot_positions
  
imports: from typing import Dict, List, Optional, Tuple, cast
  import torch
  import torch.nn.functional
  from torch.distributions import AffineTransform, TransformedDistribution
  from gluonts.core.component import validated
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 4 for calling the PiecewiseLinear obj's underlying member method code with example usage: obj.parametrize_knots(knot_spacings) and returns: knot_positions