code: def diagflat(v, k=0, sparse=None, gpu=None, chunk_size=None):
      """
      Create a two-dimensional tensor with the flattened input as a diagonal.
  
      Parameters
      ----------
      v : array_like
          Input data, which is flattened and set as the `k`-th
          diagonal of the output.
      k : int, optional
          Diagonal to set; 0, the default, corresponds to the "main" diagonal,
          a positive (negative) `k` giving the number of the diagonal above
          (below) the main.
      sparse: bool, optional
          Create sparse tensor if True, False as default
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
  
      Returns
      -------
      out : Tensor
          The 2-D output tensor.
  
      See Also
      --------
      diag : MATLAB work-alike for 1-D and 2-D tensors.
      diagonal : Return specified diagonals.
      trace : Sum along diagonals.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.diagflat([[1,2], [3,4]]).execute()
      array([[1, 0, 0, 0],
             [0, 2, 0, 0],
             [0, 0, 3, 0],
             [0, 0, 0, 4]])
  
      >>> mt.diagflat([1,2], 1).execute()
      array([[0, 1, 0],
             [0, 0, 2],
             [0, 0, 0]])
  
      """
      if not isinstance(v, Tensor):
          v = astensor(v).op.data
      return diag(
          v.flatten(),
          k=k,
          sparse=sparse,
          gpu=gpu,
          chunk_size=chunk_size,
      )
  
imports: from ..core import Tensor
  from .array import tensor
  from .diag import diag
label_desc: Use image node_id 1 for calling a global function with example usage: diagflat(v, k, sparse, gpu, chunk_size) and returns: diag