code: def _update_dict(
      dictionary,
      Y,
      code,
      A=None,
      B=None,
      verbose=False,
      random_state=None,
      positive=False,
  ):
      """Update the dense dictionary factor in place.
  
      Parameters
      ----------
      dictionary : ndarray of shape (n_components, n_features)
          Value of the dictionary at the previous iteration.
  
      Y : ndarray of shape (n_samples, n_features)
          Data matrix.
  
      code : ndarray of shape (n_samples, n_components)
          Sparse coding of the data against which to optimize the dictionary.
  
      A : ndarray of shape (n_components, n_components), default=None
          Together with `B`, sufficient stats of the online model to update the
          dictionary.
  
      B : ndarray of shape (n_features, n_components), default=None
          Together with `A`, sufficient stats of the online model to update the
          dictionary.
  
      verbose: bool, default=False
          Degree of output the procedure will print.
  
      random_state : int, RandomState instance or None, default=None
          Used for randomly initializing the dictionary. Pass an int for
          reproducible results across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      positive : bool, default=False
          Whether to enforce positivity when finding the dictionary.
  
          .. versionadded:: 0.20
      """
      n_samples, n_components = code.shape
      random_state = check_random_state(random_state)
  
      if A is None:
          A = code.T @ code
      if B is None:
          B = Y.T @ code
  
      n_unused = 0
  
      for k in range(n_components):
          if A[k, k] > 1e-6:
              # 1e-6 is arbitrary but consistent with the spams implementation
              dictionary[k] += (B[:, k] - A[k] @ dictionary) / A[k, k]
          else:
              # kth atom is almost never used -> sample a new one from the data
              newd = Y[random_state.choice(n_samples)]
  
              # add small noise to avoid making the sparse coding ill conditioned
              noise_level = 0.01 * (newd.std() or 1)  # avoid 0 std
              noise = random_state.normal(
                  0, noise_level, size=len(newd)
              )
  
              dictionary[k] = newd + noise
              code[:, k] = 0
              n_unused += 1
  
          if positive:
              np.clip(dictionary[k], 0, None, out=dictionary[k])
  
          # Projection on the constraint set ||V_k|| <= 1
          dictionary[k] /= max(linalg.norm(dictionary[k]), 1)
  
      if verbose and n_unused > 0:
          print(f"{n_unused} unused atoms resampled.")
  
imports: import itertools
  import sys
  import time
  from numbers import Integral, Real
  from warnings import warn
  import numpy
  from joblib import effective_n_jobs
  from scipy import linalg
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from ..linear_model import Lars, Lasso, LassoLars, orthogonal_mp_gram
  from ..utils import check_array, check_random_state, gen_batches, gen_even_slices
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.extmath import randomized_svd, row_norms, svd_flip
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 5 for calling a global function with example usage: _update_dict(dictionary, Y, code, A, B, verbose, random_state, positive) without return types