code: def rational_quadratic_spline(
      inputs,
      unnormalized_widths,
      unnormalized_heights,
      unnormalized_derivatives,
      inverse=False,
      left=0.0,
      right=1.0,
      bottom=0.0,
      top=1.0,
      min_bin_width=DEFAULT_MIN_BIN_WIDTH,
      min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
      min_derivative=DEFAULT_MIN_DERIVATIVE,
  ):
      if torch.min(inputs) < left or torch.max(inputs) > right:
          raise ValueError(
              "Input to a transform is not within its domain"
          )
  
      num_bins = unnormalized_widths.shape[-1]
  
      if min_bin_width * num_bins > 1.0:
          raise ValueError(
              "Minimal bin width too large for the number of bins"
          )
      if min_bin_height * num_bins > 1.0:
          raise ValueError(
              "Minimal bin height too large for the number of bins"
          )
  
      widths = F.softmax(unnormalized_widths, dim=-1)
      widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
      cumwidths = torch.cumsum(widths, dim=-1)
      cumwidths = F.pad(
          cumwidths, pad=(1, 0), mode="constant", value=0.0
      )
      cumwidths = (right - left) * cumwidths + left
      cumwidths[..., 0] = left
      cumwidths[..., -1] = right
      widths = cumwidths[..., 1:] - cumwidths[..., :-1]
  
      derivatives = min_derivative + F.softplus(
          unnormalized_derivatives
      )
  
      heights = F.softmax(unnormalized_heights, dim=-1)
      heights = (
          min_bin_height + (1 - min_bin_height * num_bins) * heights
      )
      cumheights = torch.cumsum(heights, dim=-1)
      cumheights = F.pad(
          cumheights, pad=(1, 0), mode="constant", value=0.0
      )
      cumheights = (top - bottom) * cumheights + bottom
      cumheights[..., 0] = bottom
      cumheights[..., -1] = top
      heights = cumheights[..., 1:] - cumheights[..., :-1]
  
      if inverse:
          bin_idx = searchsorted(cumheights, inputs)[..., None]
      else:
          bin_idx = searchsorted(cumwidths, inputs)[..., None]
  
      input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
      input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
  
      input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
      delta = heights / widths
      input_delta = delta.gather(-1, bin_idx)[..., 0]
  
      input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
      input_derivatives_plus_one = derivatives[..., 1:].gather(
          -1, bin_idx
      )[..., 0]
  
      input_heights = heights.gather(-1, bin_idx)[..., 0]
  
      if inverse:
          a = (inputs - input_cumheights) * (
              input_derivatives
              + input_derivatives_plus_one
              - 2 * input_delta
          ) + input_heights * (input_delta - input_derivatives)
          b = input_heights * input_derivatives - (
              inputs - input_cumheights
          ) * (
              input_derivatives
              + input_derivatives_plus_one
              - 2 * input_delta
          )
          c = -input_delta * (inputs - input_cumheights)
  
          discriminant = b.pow(2) - 4 * a * c
          assert (discriminant >= 0).all()
  
          root = (2 * c) / (-b - torch.sqrt(discriminant))
          outputs = root * input_bin_widths + input_cumwidths
  
          theta_one_minus_theta = root * (1 - root)
          denominator = input_delta + (
              (
                  input_derivatives
                  + input_derivatives_plus_one
                  - 2 * input_delta
              )
              * theta_one_minus_theta
          )
          derivative_numerator = input_delta.pow(2) * (
              input_derivatives_plus_one * root.pow(2)
              + 2 * input_delta * theta_one_minus_theta
              + input_derivatives * (1 - root).pow(2)
          )
          logabsdet = torch.log(derivative_numerator) - 2 * torch.log(
              denominator
          )
  
          return outputs, -logabsdet
      else:
          theta = (inputs - input_cumwidths) / input_bin_widths
          theta_one_minus_theta = theta * (1 - theta)
  
          numerator = input_heights * (
              input_delta * theta.pow(2)
              + input_derivatives * theta_one_minus_theta
          )
          denominator = input_delta + (
              (
                  input_derivatives
                  + input_derivatives_plus_one
                  - 2 * input_delta
              )
              * theta_one_minus_theta
          )
          outputs = input_cumheights + numerator / denominator
  
          derivative_numerator = input_delta.pow(2) * (
              input_derivatives_plus_one * theta.pow(2)
              + 2 * input_delta * theta_one_minus_theta
              + input_derivatives * (1 - theta).pow(2)
          )
          logabsdet = torch.log(derivative_numerator) - 2 * torch.log(
              denominator
          )
  
          return outputs, logabsdet
  
imports: import numpy
  import torch
  from torch.nn import functional
label_desc: Use image node_id 4 for calling a global function with example usage: rational_quadratic_spline(inputs, unnormalized_widths, unnormalized_heights, unnormalized_derivatives, inverse, left, right, bottom, top, min_bin_width, min_bin_height, min_derivative) and returns: outputs,  unknown, outputs,  logabsdet