code: def __init__(self, num_pieces: int) -> None:
      super().__init__()
  
      assert (
          isinstance(num_pieces, int) and num_pieces > 1
      ), "num_pieces should be an integer and greater than 1"
  
      self.num_pieces = num_pieces
      self.args_dim = cast(
          Dict[str, int],
          {
              "gamma": 1,
              "slopes": num_pieces,
              "knot_spacings": num_pieces,
          },
      )
  
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 1 to create a new PiecewiseLinearOutput object from inherited base classes: DistributionOutput with example: obj = PiecewiseLinearOutput(num_pieces)