parameters: (a, dtype=None, order=None)
return_statement: return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False)
docstring: Convert the input to a masked array of the given data-type.
  
      No copy is performed if the input is already an `ndarray`. If `a` is
      a subclass of `MaskedArray`, a base class `MaskedArray` is returned.
  
      Parameters
      ----------
      a : array_like
          Input data, in any form that can be converted to a masked array. This
          includes lists, lists of tuples, tuples, tuples of tuples, tuples
          of lists, ndarrays and masked arrays.
      dtype : dtype, 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 ('FORTRAN') memory
          representation.  Default is 'C'.
  
      Returns
      -------
      out : MaskedArray
          Masked array interpretation of `a`.
  
      See Also
      --------
      asanyarray : Similar to `asarray`, but conserves subclasses.
  
      Examples
      --------
      >>> x = np.arange(10.).reshape(2, 5)
      >>> x
      array([[ 0.,  1.,  2.,  3.,  4.],
             [ 5.,  6.,  7.,  8.,  9.]])
      >>> np.ma.asarray(x)
      masked_array(data =
       [[ 0.  1.  2.  3.  4.]
       [ 5.  6.  7.  8.  9.]],
                   mask =
       False,
             fill_value = 1e+20)
      >>> type(np.ma.asarray(x))
      <class 'numpy.ma.core.MaskedArray'>
docstring_summary: Convert the input to a masked array of the given data-type.
docstring_tokens: Convert the input to a masked array of the given data - type .
function: def asarray(a, dtype=None, order=None):
      """
      Convert the input to a masked array of the given data-type.
  
      No copy is performed if the input is already an `ndarray`. If `a` is
      a subclass of `MaskedArray`, a base class `MaskedArray` is returned.
  
      Parameters
      ----------
      a : array_like
          Input data, in any form that can be converted to a masked array. This
          includes lists, lists of tuples, tuples, tuples of tuples, tuples
          of lists, ndarrays and masked arrays.
      dtype : dtype, 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 ('FORTRAN') memory
          representation.  Default is 'C'.
  
      Returns
      -------
      out : MaskedArray
          Masked array interpretation of `a`.
  
      See Also
      --------
      asanyarray : Similar to `asarray`, but conserves subclasses.
  
      Examples
      --------
      >>> x = np.arange(10.).reshape(2, 5)
      >>> x
      array([[ 0.,  1.,  2.,  3.,  4.],
             [ 5.,  6.,  7.,  8.,  9.]])
      >>> np.ma.asarray(x)
      masked_array(data =
       [[ 0.  1.  2.  3.  4.]
       [ 5.  6.  7.  8.  9.]],
                   mask =
       False,
             fill_value = 1e+20)
      >>> type(np.ma.asarray(x))
      <class 'numpy.ma.core.MaskedArray'>
  
      """
      return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False)
function_tokens: def asarray ( a , dtype = None , order = None ) : return masked_array ( a , dtype = dtype , copy = False , keep_mask = True , subok = False )