code: def normalize(X, norm="l2", axis=1, copy=True):
      """Scale input vectors individually to unit norm (vector length).
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape [n_samples, n_features]
          The data to normalize, element by element.
          scipy.sparse matrices should be in CSR format to avoid an
          un-necessary copy.
  
      norm : 'l1', 'l2', or 'max', optional ('l2' by default)
          The norm to use to normalize each non zero sample (or each non-zero
          feature if axis is 0).
  
      axis : 0 or 1, optional (1 by default)
          axis used to normalize the data along. If 1, independently normalize
          each sample, otherwise (if 0) normalize each feature.
  
      copy : boolean, optional, default True
          set to False to perform inplace row normalization and avoid a
          copy (if the input is already a numpy array or a scipy.sparse
          CSR matrix and if axis is 1).
  
      Reference
      ---------
      https://github.com/scikit-learn/scikit-learn/blob/1495f69242646d239d89a5713982946b8ffcf9d9/sklearn/preprocessing/data.py#L1553
  
      """
      if norm not in ("l1", "l2", "max"):
          raise ValueError("'%s' is not a supported norm" % norm)
  
      if len(X.shape) != 2:
          raise ValueError(
              "input X must be 2D but shape={}".format(X.shape)
          )
  
      X_out = X.copy() if copy else X
      X_out = (
          X_out
          if X_out.dtype in FLOAT_DTYPES
          else X_out.astype(np.float64)
      )
  
      if axis == 0:
          X_out = X_out.T
  
      if sp.issparse(X_out):
          X_out = X_out.tocsr()
  
          if norm == "l1":
              inplace_csr_row_normalize_l1(X_out)
          elif norm == "l2":
              inplace_csr_row_normalize_l2(X_out)
          elif norm == "max":
              norms = X_out.max(axis=1).A
              norms_elementwise = norms.repeat(np.diff(X_out.indptr))
              mask = norms_elementwise != 0
              X_out.data[mask] /= norms_elementwise[mask]
      else:
          if norm == "l1":
              norms = np.abs(X_out).sum(axis=1)
          elif norm == "l2":
              norms = np.sqrt((X_out**2).sum(axis=1))
          elif norm == "max":
              norms = np.max(X_out, axis=1)
          norms[norms == 0] = 1.0
          X_out /= norms.reshape(-1, 1)
  
      if axis == 0:
          X_out = X_out.T
  
      return X_out
  
file_path: cornac/cornac/utils/common.py
imports: import numbers
  import numpy
  import scipy.sparse
  from .fast_sparse_funcs import inplace_csr_row_normalize_l1, inplace_csr_row_normalize_l2
label_desc: Use image node_id 10 for calling a global function with example usage: normalize(X, norm, axis, copy) and returns: X_out