code: def _transform_to_positive_definite(constraint):
      return ComposeTransform(
          [LowerCholeskyTransform(), CholeskyTransform().inv]
      )
  
imports: from torch.distributions.transforms import *
  from torch.distributions import biject_to, transform_to
  from torch.distributions.transforms import ComposeTransform, ExpTransform, LowerCholeskyTransform
  from torch.distributions.transforms import __all__
  from ..None import constraints
  from ..torch_transform import ComposeTransformModule
  from .affine_autoregressive import AffineAutoregressive, ConditionalAffineAutoregressive, affine_autoregressive, conditional_affine_autoregressive
  from .affine_coupling import AffineCoupling, ConditionalAffineCoupling, affine_coupling, conditional_affine_coupling
  from .basic import ELUTransform, LeakyReLUTransform, elu, leaky_relu
  from .batchnorm import BatchNorm, batchnorm
  from .block_autoregressive import BlockAutoregressive, block_autoregressive
  from .cholesky import CholeskyTransform, CorrCholeskyTransform, CorrLCholeskyTransform, CorrMatrixCholeskyTransform
  from .discrete_cosine import DiscreteCosineTransform
  from .generalized_channel_permute import ConditionalGeneralizedChannelPermute, GeneralizedChannelPermute, conditional_generalized_channel_permute, generalized_channel_permute
  from .haar import HaarTransform
  from .householder import ConditionalHouseholder, Householder, conditional_householder, householder
  from .lower_cholesky_affine import LowerCholeskyAffine
  from .matrix_exponential import ConditionalMatrixExponential, MatrixExponential, conditional_matrix_exponential, matrix_exponential
  from .neural_autoregressive import ConditionalNeuralAutoregressive, NeuralAutoregressive, conditional_neural_autoregressive, neural_autoregressive
  from .normalize import Normalize
  from .ordered import OrderedTransform
  from .permute import Permute, permute
  from .planar import ConditionalPlanar, Planar, conditional_planar, planar
  from .polynomial import Polynomial, polynomial
  from .power import PositivePowerTransform
  from .radial import ConditionalRadial, Radial, conditional_radial, radial
  from .softplus import SoftplusLowerCholeskyTransform, SoftplusTransform
  from .spline import ConditionalSpline, Spline, conditional_spline, spline
  from .spline_autoregressive import ConditionalSplineAutoregressive, SplineAutoregressive, conditional_spline_autoregressive, spline_autoregressive
  from .spline_coupling import SplineCoupling, spline_coupling
  from .sylvester import Sylvester, sylvester
  from .unit_cholesky import UnitLowerCholeskyTransform
label_desc: Use image node_id 5 for calling a global function with example usage: _transform_to_positive_definite(constraint) and returns: ComposeTransform