code: def quantile_internal(self, x, dim=None):
      if dim is not None:
          gamma = self.gamma.unsqueeze(dim=dim if dim == 0 else -1)
          knot_positions = self.knot_positions.unsqueeze(dim)
          b = self.b.unsqueeze(dim)
      else:
          gamma, knot_positions, b = (
              self.gamma,
              self.knot_positions,
              self.b,
          )
  
      x_minus_knots = x.unsqueeze(-1) - knot_positions
  
      quantile = gamma + (b * F.relu(x_minus_knots)).sum(-1)
  
      return quantile
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/distributions/piecewise_linear.py
imports: import torch
  import torch.nn.functional
  from torch.distributions import constraints, NegativeBinomial, Poisson, Distribution, TransformedDistribution, AffineTransform
  from torch.distributions.utils import broadcast_all, lazy_property
  from .utils import broadcast_shape
label_desc: Use image node_id 5 for calling the PiecewiseLinear obj's underlying member method code with example usage: obj.quantile_internal(x, dim) and returns: quantile