code: def identity(n, dtype=None, sparse=False, gpu=None, chunk_size=None):
      """
      Return the identity tensor.
  
      The identity tensor is a square array with ones on
      the main diagonal.
  
      Parameters
      ----------
      n : int
          Number of rows (and columns) in `n` x `n` output.
      dtype : data-type, optional
          Data-type of the output.  Defaults to ``float``.
      sparse: bool, optional
          Create sparse tensor if True, False as default
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      chunks : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
  
      Returns
      -------
      out : Tensor
          `n` x `n` array with its main diagonal set to one,
          and all other elements 0.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.identity(3).execute()
      array([[ 1.,  0.,  0.],
             [ 0.,  1.,  0.],
             [ 0.,  0.,  1.]])
  
      """
      return eye(
          n, dtype=dtype, sparse=sparse, gpu=gpu, chunk_size=chunk_size
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: identity(n, dtype, sparse, gpu, chunk_size) and returns: eye