code: def choose(a, choices, out=None, mode="raise"):
      """
      Construct a tensor from an index tensor and a set of tensors to choose from.
  
      First of all, if confused or uncertain, definitely look at the Examples -
      in its full generality, this function is less simple than it might
      seem from the following code description (below ndi =
      `mt.lib.index_tricks`):
  
      ``mt.choose(a,c) == mt.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
  
      But this omits some subtleties.  Here is a fully general summary:
  
      Given an "index" tensor (`a`) of integers and a sequence of `n` tensors
      (`choices`), `a` and each choice tensor are first broadcast, as necessary,
      to tensors of a common shape; calling these *Ba* and *Bchoices[i], i =
      0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
      for each `i`.  Then, a new array with shape ``Ba.shape`` is created as
      follows:
  
      * if ``mode=raise`` (the default), then, first of all, each element of
        `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
        `i` (in that range) is the value at the `(j0, j1, ..., jm)` position
        in `Ba` - then the value at the same position in the new array is the
        value in `Bchoices[i]` at that same position;
  
      * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
        integer; modular arithmetic is used to map integers outside the range
        `[0, n-1]` back into that range; and then the new array is constructed
        as above;
  
      * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
        integer; negative integers are mapped to 0; values greater than `n-1`
        are mapped to `n-1`; and then the new tensor is constructed as above.
  
      Parameters
      ----------
      a : int tensor
          This tensor must contain integers in `[0, n-1]`, where `n` is the number
          of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any
          integers are permissible.
      choices : sequence of tensors
          Choice tensors. `a` and all of the choices must be broadcastable to the
          same shape.  If `choices` is itself a tensor (not recommended), then
          its outermost dimension (i.e., the one corresponding to
          ``choices.shape[0]``) is taken as defining the "sequence".
      out : tensor, optional
          If provided, the result will be inserted into this tensor. It should
          be of the appropriate shape and dtype.
      mode : {'raise' (default), 'wrap', 'clip'}, optional
          Specifies how indices outside `[0, n-1]` will be treated:
  
            * 'raise' : an exception is raised
            * 'wrap' : value becomes value mod `n`
            * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
  
      Returns
      -------
      merged_array : Tensor
          The merged result.
  
      Raises
      ------
      ValueError: shape mismatch
          If `a` and each choice tensor are not all broadcastable to the same
          shape.
  
      See Also
      --------
      Tensor.choose : equivalent method
  
      Notes
      -----
      To reduce the chance of misinterpretation, even though the following
      "abuse" is nominally supported, `choices` should neither be, nor be
      thought of as, a single tensor, i.e., the outermost sequence-like container
      should be either a list or a tuple.
  
      Examples
      --------
  
      >>> import mars.tensor as mt
  
      >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
      ...   [20, 21, 22, 23], [30, 31, 32, 33]]
      >>> mt.choose([2, 3, 1, 0], choices
      ... # the first element of the result will be the first element of the
      ... # third (2+1) "array" in choices, namely, 20; the second element
      ... # will be the second element of the fourth (3+1) choice array, i.e.,
      ... # 31, etc.
      ... ).execute()
      array([20, 31, 12,  3])
      >>> mt.choose([2, 4, 1, 0], choices, mode='clip').execute() # 4 goes to 3 (4-1)
      array([20, 31, 12,  3])
      >>> # because there are 4 choice arrays
      >>> mt.choose([2, 4, 1, 0], choices, mode='wrap').execute() # 4 goes to (4 mod 4)
      array([20,  1, 12,  3])
      >>> # i.e., 0
  
      A couple examples illustrating how choose broadcasts:
  
      >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
      >>> choices = [-10, 10]
      >>> mt.choose(a, choices).execute()
      array([[ 10, -10,  10],
             [-10,  10, -10],
             [ 10, -10,  10]])
  
      >>> # With thanks to Anne Archibald
      >>> a = mt.array([0, 1]).reshape((2,1,1))
      >>> c1 = mt.array([1, 2, 3]).reshape((1,3,1))
      >>> c2 = mt.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
      >>> mt.choose(a, (c1, c2)).execute() # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
      array([[[ 1,  1,  1,  1,  1],
              [ 2,  2,  2,  2,  2],
              [ 3,  3,  3,  3,  3]],
             [[-1, -2, -3, -4, -5],
              [-1, -2, -3, -4, -5],
              [-1, -2, -3, -4, -5]]])
  
      """
      a = astensor(a, dtype="i8")
      choices = [astensor(c) for c in choices]
  
      dtype = np.result_type(*[c.dtype for c in choices])
      op = TensorChoose(mode=mode, dtype=dtype)
      return op(a, choices, out=out)
  
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import FieldTypes, KeyField, ListField, StringField
  from ..utils import broadcast_shape, check_out_param
  from ..operands import TensorOperand, TensorOperandMixin
  from ..datasource import tensor
  from ..core import Tensor, TensorOrder
  from ..array_utils import as_same_device, device
label_desc: Use image node_id 1 for calling a global function with example usage: choose(a, choices, out, mode) and returns: op