parameters: (input_shape, axes)
return_statement: return gen_data_flow_ops.dynamic_stitch(  # [2, 1, 1, 7]
        [range(input_rank),                   # [0, 1, 2, 3]
         axes],                               # [1, 2]
        [input_shape,                         # [2, 3, 5, 7]
         array_ops.fill(axes_shape, 1)])
docstring: Helper function for reduction ops.
  
    Args:
      input_shape: 1-D Tensor, the shape of the Tensor being reduced.
      axes: 1-D Tensor, the reduction axes.
    Returns:
      A 1-D Tensor, the output shape as if keep_dims were set to True.
docstring_summary: Helper function for reduction ops.
docstring_tokens: Helper function for reduction ops .
function: def reduced_shape(input_shape, axes):
    """Helper function for reduction ops.
  
    Args:
      input_shape: 1-D Tensor, the shape of the Tensor being reduced.
      axes: 1-D Tensor, the reduction axes.
    Returns:
      A 1-D Tensor, the output shape as if keep_dims were set to True.
    """
    # Example:
    # cast needed for SparseTensor reductions
    input_shape = to_int32(input_shape)       # [2, 3, 5, 7]
    axes = to_int32(axes)                     # [1, 2]
  
    input_rank = array_ops.size(input_shape)  # 4
    axes = (axes + input_rank) % input_rank
    axes_shape = array_ops.shape(axes)        # [2]
    return gen_data_flow_ops.dynamic_stitch(  # [2, 1, 1, 7]
        [range(input_rank),                   # [0, 1, 2, 3]
         axes],                               # [1, 2]
        [input_shape,                         # [2, 3, 5, 7]
         array_ops.fill(axes_shape, 1)])
function_tokens: def reduced_shape ( input_shape , axes ) : # Example: # cast needed for SparseTensor reductions input_shape = to_int32 ( input_shape ) # [2, 3, 5, 7] axes = to_int32 ( axes ) # [1, 2] input_rank = array_ops . size ( input_shape ) # 4 axes = ( axes + input_rank ) % input_rank axes_shape = array_ops . shape ( axes ) # [2] return gen_data_flow_ops . dynamic_stitch ( # [2, 1, 1, 7] [ range ( input_rank ) , # [0, 1, 2, 3] axes ] , # [1, 2] [ input_shape , # [2, 3, 5, 7] array_ops . fill ( axes_shape , 1 ) ] )