code: def reshape(self, *args, **kwargs):
      is_array = isinstance(self, sparray)
      shape = check_shape(args, self.shape, allow_1d=is_array)
      order, copy = check_reshape_kwargs(kwargs)
  
      # Return early if reshape is not required
      if shape == self.shape:
          if copy:
              return self.copy()
          else:
              return self
  
      # When reducing the number of dimensions, we need to be careful about
      # index overflow. This is why we can't simply call
      # `np.ravel_multi_index()` followed by `np.unravel_index()` here.
      flat_indices = _ravel_indices(
          self.indices, self.shape, order=order
      )
      if len(shape) == 2:
          if order == "C":
              new_indices = divmod(flat_indices, shape[1])
          else:
              new_indices = divmod(flat_indices, shape[0])[::-1]
      else:
          new_indices = np.unravel_index(
              flat_indices, shape, order=order
          )
  
      # Handle copy here rather than passing on to the constructor so that no
      # copy will be made of `new_indices` regardless.
      if copy:
          new_data = self.data.copy()
      else:
          new_data = self.data
  
      return self.__class__(
          (new_data, new_indices), shape=shape, copy=False
      )
  
imports: import math
  from warnings import warn
  import numpy
  from ._matrix import spmatrix
  from ._sparsetools import coo_tocsr, coo_todense, coo_matvec
  from ._base import issparse, SparseEfficiencyWarning, _spbase, sparray
  from ._data import _data_matrix, _minmax_mixin
  from ._sputils import upcast, upcast_char, to_native, isshape, getdtype, getdata, downcast_intp_index, get_index_dtype, check_shape, check_reshape_kwargs
  import operator
label_desc: Use image node_id 6 for calling the _coo_base obj's underlying member method code with example usage: obj.reshape() and returns: self, self, self