code: def reshape(a, newshape, order="C"):
      """
      Gives a new shape to a tensor without changing its data.
  
      Parameters
      ----------
      a : array_like
          Tensor to be reshaped.
      newshape : int or tuple of ints
          The new shape should be compatible with the original shape. If
          an integer, then the result will be a 1-D tensor of that length.
          One shape dimension can be -1. In this case, the value is
          inferred from the length of the tensor and remaining dimensions.
      order : {'C', 'F', 'A'}, optional
          Read the elements of `a` using this index order, and place the
          elements into the reshaped array using this index order.  'C'
          means to read / write the elements using C-like index order,
          with the last axis index changing fastest, back to the first
          axis index changing slowest. 'F' means to read / write the
          elements using Fortran-like index order, with the first index
          changing fastest, and the last index changing slowest. Note that
          the 'C' and 'F' options take no account of the memory layout of
          the underlying array, and only refer to the order of indexing.
          'A' means to read / write the elements in Fortran-like index
          order if `a` is Fortran *contiguous* in memory, C-like order
          otherwise.
  
      Returns
      -------
      reshaped_array : Tensor
          This will be a new view object if possible; otherwise, it will
          be a copy.
  
      See Also
      --------
      Tensor.reshape : Equivalent method.
  
      Notes
      -----
      It is not always possible to change the shape of a tensor without
      copying the data. If you want an error to be raised when the data is copied,
      you should assign the new shape to the shape attribute of the array::
  
      >>> import mars.tensor as mt
  
      >>> a = mt.arange(6).reshape((3, 2))
      >>> a.execute()
      array([[0, 1],
             [2, 3],
             [4, 5]])
  
      You can think of reshaping as first raveling the tensor (using the given
      index order), then inserting the elements from the raveled tensor into the
      new tensor using the same kind of index ordering as was used for the
      raveling.
  
      >>> mt.reshape(a, (2, 3)).execute()
      array([[0, 1, 2],
             [3, 4, 5]])
      >>> mt.reshape(mt.ravel(a), (2, 3)).execute()
      array([[0, 1, 2],
             [3, 4, 5]])
  
      Examples
      --------
      >>> a = mt.array([[1,2,3], [4,5,6]])
      >>> mt.reshape(a, 6).execute()
      array([1, 2, 3, 4, 5, 6])
  
      >>> mt.reshape(a, (3,-1)).execute()       # the unspecified value is inferred to be 2
      array([[1, 2],
             [3, 4],
             [5, 6]])
      """
      a = astensor(a)
  
      if np.isnan(sum(a.shape)):
          # some shape is nan
          new_shape = (
              [newshape]
              if isinstance(newshape, int)
              else list(newshape)
          )
          # if -1 exists in newshape, just treat it as unknown shape
          new_shape = [s if s != -1 else np.nan for s in new_shape]
          out_shape = tuple(new_shape)
      else:
          out_shape = newshape = calc_shape(a.size, newshape)
          if a.size != np.prod(newshape):
              raise ValueError(
                  f"cannot reshape array of size {a.size} into shape {newshape}"
              )
  
      tensor_order = get_order(order, a.order, available_options="CFA")
  
      if a.shape == newshape and (
          a.ndim <= 1 or (a.ndim > 1 and tensor_order == a.order)
      ):
          # does not need to reshape
          return a
      return _reshape(
          a,
          newshape,
          order=order,
          tensor_order=tensor_order,
          out_shape=out_shape,
      )
  
imports: import itertools
  import logging
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import FieldTypes, KeyField, TupleField, StringField
  from ...utils import has_unknown_shape
  from ..array_utils import as_same_device, device
  from ..datasource import tensor
  from ..operands import TensorOperandMixin, TensorMapReduceOperand, TensorShuffleProxy
  from ..utils import get_order, decide_chunk_sizes
label_desc: Use image node_id 2 for calling a global function with example usage: reshape(a, newshape, order) and returns: _reshape, a