parameters: (
      size, name, dtype, initial_diagonal_values=None,
      initial_overall_scale_log=0.)
return_statement: return array_ops.identity(
        sign_magnitude_positive_definite(
            raw=raw_values,
            off_diagonal_scale=variable_scope.get_variable(
                name + "_off_diagonal_scale",
                dtype=dtype,
                initializer=constant_op.constant(-5., dtype=dtype)),
            overall_scale=ops.convert_to_tensor(
                initial_overall_scale_log, dtype=dtype) +
            variable_scope.get_variable(
                name + "_overall_scale",
                dtype=dtype,
                shape=[],
                initializer=init_ops.zeros_initializer())),
        name=name)
docstring: Construct a Variable-parameterized positive definite matrix.
  
    Useful for parameterizing covariance matrices.
  
    Args:
      size: The size of the main diagonal, the returned matrix having shape [size
          x size].
      name: The name to use when defining variables and ops.
      dtype: The floating point data type to use.
      initial_diagonal_values: A Tensor with shape [size] with initial values for
          the diagonal values of the returned matrix. Must be positive.
      initial_overall_scale_log: Initial value of the bias term for every element
          of the matrix in log space.
    Returns:
      A Variable-parameterized covariance matrix with shape [size x size].
docstring_summary: Construct a Variable-parameterized positive definite matrix.
docstring_tokens: Construct a Variable - parameterized positive definite matrix .
function: def variable_covariance_matrix(
      size, name, dtype, initial_diagonal_values=None,
      initial_overall_scale_log=0.):
    """Construct a Variable-parameterized positive definite matrix.
  
    Useful for parameterizing covariance matrices.
  
    Args:
      size: The size of the main diagonal, the returned matrix having shape [size
          x size].
      name: The name to use when defining variables and ops.
      dtype: The floating point data type to use.
      initial_diagonal_values: A Tensor with shape [size] with initial values for
          the diagonal values of the returned matrix. Must be positive.
      initial_overall_scale_log: Initial value of the bias term for every element
          of the matrix in log space.
    Returns:
      A Variable-parameterized covariance matrix with shape [size x size].
    """
    raw_values = variable_scope.get_variable(
        name + "_pre_transform",
        dtype=dtype,
        shape=[size, size],
        initializer=init_ops.zeros_initializer())
    if initial_diagonal_values is not None:
      raw_values += array_ops.matrix_diag(math_ops.log(initial_diagonal_values))
    return array_ops.identity(
        sign_magnitude_positive_definite(
            raw=raw_values,
            off_diagonal_scale=variable_scope.get_variable(
                name + "_off_diagonal_scale",
                dtype=dtype,
                initializer=constant_op.constant(-5., dtype=dtype)),
            overall_scale=ops.convert_to_tensor(
                initial_overall_scale_log, dtype=dtype) +
            variable_scope.get_variable(
                name + "_overall_scale",
                dtype=dtype,
                shape=[],
                initializer=init_ops.zeros_initializer())),
        name=name)
function_tokens: def variable_covariance_matrix ( size , name , dtype , initial_diagonal_values = None , initial_overall_scale_log = 0. ) : raw_values = variable_scope . get_variable ( name + "_pre_transform" , dtype = dtype , shape = [ size , size ] , initializer = init_ops . zeros_initializer ( ) ) if initial_diagonal_values is not None : raw_values += array_ops . matrix_diag ( math_ops . log ( initial_diagonal_values ) ) return array_ops . identity ( sign_magnitude_positive_definite ( raw = raw_values , off_diagonal_scale = variable_scope . get_variable ( name + "_off_diagonal_scale" , dtype = dtype , initializer = constant_op . constant ( - 5. , dtype = dtype ) ) , overall_scale = ops . convert_to_tensor ( initial_overall_scale_log , dtype = dtype ) + variable_scope . get_variable ( name + "_overall_scale" , dtype = dtype , shape = [ ] , initializer = init_ops . zeros_initializer ( ) ) ) , name = name )