parameters: (a, order='K')
return_statement: return array(a, order=order, copy=True)
docstring: Return an array copy of the given object.
  
      Parameters
      ----------
      a : array_like
          Input data.
      order : {'C', 'F', 'A', 'K'}, optional
          Controls the memory layout of the copy. '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. (Note that this function and :meth:ndarray.copy are very
          similar, but have different default values for their order=
          arguments.)
  
      Returns
      -------
      arr : ndarray
          Array interpretation of `a`.
  
      Notes
      -----
      This is equivalent to
  
      >>> np.array(a, copy=True)                              #doctest: +SKIP
  
      Examples
      --------
      Create an array x, with a reference y and a copy z:
  
      >>> x = np.array([1, 2, 3])
      >>> y = x
      >>> z = np.copy(x)
  
      Note that, when we modify x, y changes, but not z:
  
      >>> x[0] = 10
      >>> x[0] == y[0]
      True
      >>> x[0] == z[0]
      False
docstring_summary: Return an array copy of the given object.
docstring_tokens: Return an array copy of the given object .
function: def copy(a, order='K'):
      """
      Return an array copy of the given object.
  
      Parameters
      ----------
      a : array_like
          Input data.
      order : {'C', 'F', 'A', 'K'}, optional
          Controls the memory layout of the copy. '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. (Note that this function and :meth:ndarray.copy are very
          similar, but have different default values for their order=
          arguments.)
  
      Returns
      -------
      arr : ndarray
          Array interpretation of `a`.
  
      Notes
      -----
      This is equivalent to
  
      >>> np.array(a, copy=True)                              #doctest: +SKIP
  
      Examples
      --------
      Create an array x, with a reference y and a copy z:
  
      >>> x = np.array([1, 2, 3])
      >>> y = x
      >>> z = np.copy(x)
  
      Note that, when we modify x, y changes, but not z:
  
      >>> x[0] = 10
      >>> x[0] == y[0]
      True
      >>> x[0] == z[0]
      False
  
      """
      return array(a, order=order, copy=True)
function_tokens: def copy ( a , order = 'K' ) : return array ( a , order = order , copy = True )