parameters: (a, dtype=None, order=None)
return_statement: return array(a, dtype, copy=False, order=order, subok=True)
docstring: Convert the input to an ndarray, but pass ndarray subclasses through.
  
      Parameters
      ----------
      a : array_like
          Input data, in any form that can be converted to an array.  This
          includes scalars, lists, lists of tuples, tuples, tuples of tuples,
          tuples of lists, and ndarrays.
      dtype : data-type, optional
          By default, the data-type is inferred from the input data.
      order : {'C', 'F'}, optional
          Whether to use row-major ('C') or column-major ('F') memory
          representation.  Defaults to 'C'.
  
      Returns
      -------
      out : ndarray or an ndarray subclass
          Array interpretation of `a`.  If `a` is an ndarray or a subclass
          of ndarray, it is returned as-is and no copy is performed.
  
      See Also
      --------
      asarray : Similar function which always returns ndarrays.
      ascontiguousarray : Convert input to a contiguous array.
      asfarray : Convert input to a floating point ndarray.
      asfortranarray : Convert input to an ndarray with column-major
                       memory order.
      asarray_chkfinite : Similar function which checks input for NaNs and
                          Infs.
      fromiter : Create an array from an iterator.
      fromfunction : Construct an array by executing a function on grid
                     positions.
  
      Examples
      --------
      Convert a list into an array:
  
      >>> a = [1, 2]
      >>> np.asanyarray(a)
      array([1, 2])
  
      Instances of `ndarray` subclasses are passed through as-is:
  
      >>> a = np.matrix([1, 2])
      >>> np.asanyarray(a) is a
      True
docstring_summary: Convert the input to an ndarray, but pass ndarray subclasses through.
docstring_tokens: Convert the input to an ndarray but pass ndarray subclasses through .
function: def asanyarray(a, dtype=None, order=None):
      """
      Convert the input to an ndarray, but pass ndarray subclasses through.
  
      Parameters
      ----------
      a : array_like
          Input data, in any form that can be converted to an array.  This
          includes scalars, lists, lists of tuples, tuples, tuples of tuples,
          tuples of lists, and ndarrays.
      dtype : data-type, optional
          By default, the data-type is inferred from the input data.
      order : {'C', 'F'}, optional
          Whether to use row-major ('C') or column-major ('F') memory
          representation.  Defaults to 'C'.
  
      Returns
      -------
      out : ndarray or an ndarray subclass
          Array interpretation of `a`.  If `a` is an ndarray or a subclass
          of ndarray, it is returned as-is and no copy is performed.
  
      See Also
      --------
      asarray : Similar function which always returns ndarrays.
      ascontiguousarray : Convert input to a contiguous array.
      asfarray : Convert input to a floating point ndarray.
      asfortranarray : Convert input to an ndarray with column-major
                       memory order.
      asarray_chkfinite : Similar function which checks input for NaNs and
                          Infs.
      fromiter : Create an array from an iterator.
      fromfunction : Construct an array by executing a function on grid
                     positions.
  
      Examples
      --------
      Convert a list into an array:
  
      >>> a = [1, 2]
      >>> np.asanyarray(a)
      array([1, 2])
  
      Instances of `ndarray` subclasses are passed through as-is:
  
      >>> a = np.matrix([1, 2])
      >>> np.asanyarray(a) is a
      True
  
      """
      return array(a, dtype, copy=False, order=order, subok=True)
function_tokens: def asanyarray ( a , dtype = None , order = None ) : return array ( a , dtype , copy = False , order = order , subok = True )