code: def normalize(X, norm="l2", axis=1, copy=True, return_norm=False):
      """
      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 tensor and if axis is 1).
  
      return_norm : boolean, default False
          whether to return the computed norms
  
      Returns
      -------
      X : {array-like, sparse matrix}, shape [n_samples, n_features]
          Normalized input X.
  
      norms : Tensor, shape [n_samples] if axis=1 else [n_features]
          A tensor of norms along given axis for X.
          When X is sparse, a NotImplementedError will be raised
          for norm 'l1' or 'l2'.
  
      See also
      --------
      Normalizer: Performs normalization using the ``Transformer`` API
          (e.g. as part of a preprocessing :class:`mars.learn.pipeline.Pipeline`).
      """
      if norm not in ("l1", "l2", "max"):
          raise ValueError(f"'{norm}' is not a supported norm")
      if axis not in (0, 1):
          raise ValueError(f"'{axis}' is not a supported axis")
      op = TensorNormalize(
          norm=norm, axis=axis, return_norm=return_norm, dtype=X.dtype
      )
      return op(X, copy=copy)
  
imports: import numpy
  from ...None import opcodes
  from ...core import ExecutableTuple, recursive_tile
  from ...serialization.serializables import KeyField, StringField, Int32Field, BoolField
  from ...tensor.operands import TensorOperand, TensorOperandMixin
  from ...tensor.core import TensorOrder
  from ...tensor.array_utils import as_same_device, device, sparse, issparse
  from ...None import tensor
  from ..utils import check_array
label_desc: Use image node_id 1 for calling a global function with example usage: normalize(X, norm, axis, copy, return_norm) and returns: op