code: def l1_min_c(
      X,
      y,
      loss="squared_hinge",
      fit_intercept=True,
      intercept_scaling=1.0,
  ):
      """
      Return the lowest bound for C 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 or sparse matrix, shape = [n_samples, n_features]
          Training vector, where n_samples in the number of samples and
          n_features is the number of features.
  
      y : array, 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.
          'l2' is accepted as an alias for 'squared_hinge', for backward
          compatibility reasons, but should not be used in new code.
  
      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
          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
      """
      if loss not in ("squared_hinge", "log"):
          raise ValueError(
              'loss type not in ("squared_hinge", "log", "l2")'
          )
  
      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 = intercept_scaling * np.ones((np.size(y), 1))
          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 warnings import warn
  import numpy
  from ..preprocessing import LabelBinarizer
  from ..utils.validation import check_consistent_length, check_array
  from ..utils.extmath import safe_sparse_dot
label_desc: Use image node_id 1 for calling a global function with example usage: l1_min_c(X, y, loss, fit_intercept, intercept_scaling) and returns: unknown, unknown