code: def l1_min_c(
      X,
      y,
      *,
      loss="squared_hinge",
      fit_intercept=True,
      intercept_scaling=1.0,
  ):
      """Return the lowest bound for C.
  
      The lower bound for C is computed such that for C in (l1_min_C, infinity)
      the model is guaranteed not to be empty. This applies to l1 penalized
      classifiers, such as LinearSVC with penalty='l1' and
      linear_model.LogisticRegression with penalty='l1'.
  
      This value is valid if class_weight parameter in fit() is not set.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vector, where `n_samples` is the number of samples and
          `n_features` is the number of features.
  
      y : array-like of shape (n_samples,)
          Target vector relative to X.
  
      loss : {'squared_hinge', 'log'}, default='squared_hinge'
          Specifies the loss function.
          With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
          With 'log' it is the loss of logistic regression models.
  
      fit_intercept : bool, default=True
          Specifies if the intercept should be fitted by the model.
          It must match the fit() method parameter.
  
      intercept_scaling : float, default=1.0
          When fit_intercept is True, instance vector x becomes
          [x, intercept_scaling],
          i.e. a "synthetic" feature with constant value equals to
          intercept_scaling is appended to the instance vector.
          It must match the fit() method parameter.
  
      Returns
      -------
      l1_min_c : float
          Minimum value for C.
      """
  
      X = check_array(X, accept_sparse="csc")
      check_consistent_length(X, y)
  
      Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
      # maximum absolute value over classes and features
      den = np.max(np.abs(safe_sparse_dot(Y, X)))
      if fit_intercept:
          bias = np.full(
              (np.size(y), 1),
              intercept_scaling,
              dtype=np.array(intercept_scaling).dtype,
          )
          den = max(den, abs(np.dot(Y, bias)).max())
  
      if den == 0.0:
          raise ValueError(
              "Ill-posed l1_min_c calculation: l1 will always "
              "select zero coefficients for this data"
          )
      if loss == "squared_hinge":
          return 0.5 / den
      else:  # loss == 'log':
          return 2.0 / den
  
imports: from numbers import Real
  import numpy
  from ..preprocessing import LabelBinarizer
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_array, check_consistent_length
label_desc: Use image node_id 1 for calling a global function with example usage: l1_min_c(X, y) and returns: unknown, unknown