code: def make_nonnegative(X, min_value=0):
      """Ensure `X.min()` >= `min_value`.
  
      Parameters
      ----------
      X : array-like
          The matrix to make non-negative.
      min_value : float, default=0
          The threshold value.
  
      Returns
      -------
      array-like
          The thresholded array.
  
      Raises
      ------
      ValueError
          When X is sparse.
      """
      min_ = X.min()
      if min_ < min_value:
          if sparse.issparse(X):
              raise ValueError(
                  "Cannot make the data matrix"
                  " nonnegative because it is sparse."
                  " Adding a value to every entry would"
                  " make it no longer sparse."
              )
          X = X + (min_value - min_)
      return X
  
imports: import warnings
  import numpy
  from scipy import linalg, sparse
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, get_namespace
  from ._logistic_sigmoid import _log_logistic_sigmoid
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 14 for calling a global function with example usage: make_nonnegative(X, min_value) and returns: X