parameters: (op, unused_output_indices_grad, output_values_grad)
return_statement: return (None,
            array_ops.gather(output_values_grad, inverted_permutation),
            None)
docstring: Gradients for the SparseReorder op.
  
    Args:
      op: the SparseReorder op
      unused_output_indices_grad: the incoming gradients of the output indices
      output_values_grad: the incoming gradients of the output values
  
    Returns:
      Gradient for each of the 3 input tensors:
        (input_indices, input_values, input_shape)
      The gradients for input_indices and input_shape is None.
docstring_summary: Gradients for the SparseReorder op.
docstring_tokens: Gradients for the SparseReorder op .
function: def _SparseReorderGrad(op, unused_output_indices_grad, output_values_grad):
    """Gradients for the SparseReorder op.
  
    Args:
      op: the SparseReorder op
      unused_output_indices_grad: the incoming gradients of the output indices
      output_values_grad: the incoming gradients of the output values
  
    Returns:
      Gradient for each of the 3 input tensors:
        (input_indices, input_values, input_shape)
      The gradients for input_indices and input_shape is None.
    """
    input_indices = op.inputs[0]
    input_shape = op.inputs[2]
  
    num_entries = array_ops.shape(input_indices)[0]
    entry_indices = math_ops.range(num_entries)
    sp_unordered = ops.SparseTensor(input_indices, entry_indices, input_shape)
    sp_ordered = sparse_ops.sparse_reorder(sp_unordered)
    inverted_permutation = array_ops.invert_permutation(sp_ordered.values)
  
    return (None,
            array_ops.gather(output_values_grad, inverted_permutation),
            None)
function_tokens: def _SparseReorderGrad ( op , unused_output_indices_grad , output_values_grad ) : input_indices = op . inputs [ 0 ] input_shape = op . inputs [ 2 ] num_entries = array_ops . shape ( input_indices ) [ 0 ] entry_indices = math_ops . range ( num_entries ) sp_unordered = ops . SparseTensor ( input_indices , entry_indices , input_shape ) sp_ordered = sparse_ops . sparse_reorder ( sp_unordered ) inverted_permutation = array_ops . invert_permutation ( sp_ordered . values ) return ( None , array_ops . gather ( output_values_grad , inverted_permutation ) , None )