class_docstr: Frozen Statespace Model
  
  Takes a snapshot of a Statespace model.
  
  Parameters
  ----------
  model : Representation
      A Statespace representation
  
  Attributes
  ----------
  nobs : int
      Number of observations.
  k_endog : int
      The dimension of the observation series.
  k_states : int
      The dimension of the unobserved state process.
  k_posdef : int
      The dimension of a guaranteed positive definite
      covariance matrix describing the shocks in the
      measurement equation.
  dtype : dtype
      Datatype of representation matrices
  prefix : str
      BLAS prefix of representation matrices
  shapes : dictionary of name:tuple
      A dictionary recording the shapes of each of
      the representation matrices as tuples.
  endog : ndarray
      The observation vector.
  design : ndarray
      The design matrix, :math:`Z`.
  obs_intercept : ndarray
      The intercept for the observation equation, :math:`d`.
  obs_cov : ndarray
      The covariance matrix for the observation equation :math:`H`.
  transition : ndarray
      The transition matrix, :math:`T`.
  state_intercept : ndarray
      The intercept for the transition equation, :math:`c`.
  selection : ndarray
      The selection matrix, :math:`R`.
  state_cov : ndarray
      The covariance matrix for the state equation :math:`Q`.
  missing : array of bool
      An array of the same size as `endog`, filled
      with boolean values that are True if the
      corresponding entry in `endog` is NaN and False
      otherwise.
  nmissing : array of int
      An array of size `nobs`, where the ith entry
      is the number (between 0 and `k_endog`) of NaNs in
      the ith row of the `endog` array.
  time_invariant : bool
      Whether or not the representation matrices are time-invariant
  initialization : Initialization object
      Kalman filter initialization method.
  initial_state : array_like
      The state vector used to initialize the Kalamn filter.
  initial_state_cov : array_like
      The state covariance matrix used to initialize the Kalamn filter.
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code: def __init__(self, model):
      # Initialize all attributes to None
      for name in self._attributes:
          setattr(self, name, None)
  
      # Update the representation attributes
      self.update_representation(model)
  
imports: import warnings
  import numpy
  from .tools import find_best_blas_type, validate_matrix_shape, validate_vector_shape
  from .initialization import Initialization
  from .None import tools
label_desc: Use image node_id 1 to create a new FrozenRepresentation object  with example: obj = FrozenRepresentation(model)