parameters: (a, dtype=None, order='K', subok=True, shape=None)
return_statement: return res
docstring: Return an array of zeros with the same shape and type as a given array.
  
      Parameters
      ----------
      a : array_like
          The shape and data-type of `a` define these same attributes of
          the returned array.
      dtype : data-type, optional
          Overrides the data type of the result.
  
          .. versionadded:: 1.6.0
      order : {'C', 'F', 'A', or 'K'}, optional
          Overrides the memory layout of the result. 'C' means C-order,
          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
          'C' otherwise. 'K' means match the layout of `a` as closely
          as possible.
  
          .. versionadded:: 1.6.0
      subok : bool, optional.
          If True, then the newly created array will use the sub-class
          type of 'a', otherwise it will be a base-class array. Defaults
          to True.
      shape : int or sequence of ints, optional.
          Overrides the shape of the result. If order='K' and the number of
          dimensions is unchanged, will try to keep order, otherwise,
          order='C' is implied.
  
          .. versionadded:: 1.17.0
  
      Returns
      -------
      out : ndarray
          Array of zeros with the same shape and type as `a`.
  
      See Also
      --------
      empty_like : Return an empty array with shape and type of input.
      ones_like : Return an array of ones with shape and type of input.
      full_like : Return a new array with shape of input filled with value.
      zeros : Return a new array setting values to zero.
  
      Examples
      --------
      >>> x = np.arange(6)
      >>> x = x.reshape((2, 3))
      >>> x
      array([[0, 1, 2],
             [3, 4, 5]])
      >>> np.zeros_like(x)
      array([[0, 0, 0],
             [0, 0, 0]])
  
      >>> y = np.arange(3, dtype=float)
      >>> y
      array([0., 1., 2.])
      >>> np.zeros_like(y)
      array([0.,  0.,  0.])
docstring_summary: Return an array of zeros with the same shape and type as a given array.
docstring_tokens: Return an array of zeros with the same shape and type as a given array .
function: def zeros_like(a, dtype=None, order='K', subok=True, shape=None):
      """
      Return an array of zeros with the same shape and type as a given array.
  
      Parameters
      ----------
      a : array_like
          The shape and data-type of `a` define these same attributes of
          the returned array.
      dtype : data-type, optional
          Overrides the data type of the result.
  
          .. versionadded:: 1.6.0
      order : {'C', 'F', 'A', or 'K'}, optional
          Overrides the memory layout of the result. 'C' means C-order,
          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
          'C' otherwise. 'K' means match the layout of `a` as closely
          as possible.
  
          .. versionadded:: 1.6.0
      subok : bool, optional.
          If True, then the newly created array will use the sub-class
          type of 'a', otherwise it will be a base-class array. Defaults
          to True.
      shape : int or sequence of ints, optional.
          Overrides the shape of the result. If order='K' and the number of
          dimensions is unchanged, will try to keep order, otherwise,
          order='C' is implied.
  
          .. versionadded:: 1.17.0
  
      Returns
      -------
      out : ndarray
          Array of zeros with the same shape and type as `a`.
  
      See Also
      --------
      empty_like : Return an empty array with shape and type of input.
      ones_like : Return an array of ones with shape and type of input.
      full_like : Return a new array with shape of input filled with value.
      zeros : Return a new array setting values to zero.
  
      Examples
      --------
      >>> x = np.arange(6)
      >>> x = x.reshape((2, 3))
      >>> x
      array([[0, 1, 2],
             [3, 4, 5]])
      >>> np.zeros_like(x)
      array([[0, 0, 0],
             [0, 0, 0]])
  
      >>> y = np.arange(3, dtype=float)
      >>> y
      array([0., 1., 2.])
      >>> np.zeros_like(y)
      array([0.,  0.,  0.])
  
      """
      res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
      # needed instead of a 0 to get same result as zeros for for string dtypes
      z = zeros(1, dtype=res.dtype)
      multiarray.copyto(res, z, casting='unsafe')
      return res
function_tokens: def zeros_like ( a , dtype = None , order = 'K' , subok = True , shape = None ) : res = empty_like ( a , dtype = dtype , order = order , subok = subok , shape = shape ) # needed instead of a 0 to get same result as zeros for for string dtypes z = zeros ( 1 , dtype = res . dtype ) multiarray . copyto ( res , z , casting = 'unsafe' ) return res