parameters: (input_tensor, fft_rank)
return_statement: return _ops.convert_to_tensor(fft_shape.as_list(), _dtypes.int32)
docstring: Infers the `fft_length` argument for a `rank` RFFT from `input_tensor`.
docstring_summary: Infers the `fft_length` argument for a `rank` RFFT from `input_tensor`.
docstring_tokens: Infers the fft_length argument for a rank RFFT from input_tensor .
function: def _infer_fft_length_for_rfft(input_tensor, fft_rank):
    """Infers the `fft_length` argument for a `rank` RFFT from `input_tensor`."""
    # A TensorShape for the inner fft_rank dimensions.
    fft_shape = input_tensor.get_shape()[-fft_rank:]
  
    # If any dim is unknown, fall back to tensor-based math.
    if not fft_shape.is_fully_defined():
      return _array_ops.shape(input_tensor)[-fft_rank:]
  
    # Otherwise, return a constant.
    return _ops.convert_to_tensor(fft_shape.as_list(), _dtypes.int32)
function_tokens: def _infer_fft_length_for_rfft ( input_tensor , fft_rank ) : # A TensorShape for the inner fft_rank dimensions. fft_shape = input_tensor . get_shape ( ) [ - fft_rank : ] # If any dim is unknown, fall back to tensor-based math. if not fft_shape . is_fully_defined ( ) : return _array_ops . shape ( input_tensor ) [ - fft_rank : ] # Otherwise, return a constant. return _ops . convert_to_tensor ( fft_shape . as_list ( ) , _dtypes . int32 )