code: def softmax(X, copy=True):
      """
      Calculate the softmax function.
  
      The softmax function is calculated by
      np.exp(X) / np.sum(np.exp(X), axis=1)
  
      This will cause overflow when large values are exponentiated.
      Hence the largest value in each row is subtracted from each data
      point to prevent this.
  
      Parameters
      ----------
      X : array-like of float of shape (M, N)
          Argument to the logistic function.
  
      copy : bool, default=True
          Copy X or not.
  
      Returns
      -------
      out : ndarray of shape (M, N)
          Softmax function evaluated at every point in x.
      """
      xp, is_array_api_compliant = get_namespace(X)
      if copy:
          X = xp.asarray(X, copy=True)
      max_prob = xp.reshape(xp.max(X, axis=1), (-1, 1))
      X -= max_prob
  
      if _is_numpy_namespace(xp):
          # optimization for NumPy arrays
          np.exp(X, out=np.asarray(X))
      else:
          # array_api does not have `out=`
          X = xp.exp(X)
  
      sum_prob = xp.reshape(xp.sum(X, axis=1), (-1, 1))
      X /= sum_prob
      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 13 for calling a global function with example usage: softmax(X, copy) and returns: X