public RaggedTensor string_split_v2(Tensor input, string sep = " ", int maxsplit = -1, string name = null)
{
return(tf_with(ops.name_scope(name, "StringSplit"), scope =>
{
var sep_tensor = ops.convert_to_tensor(sep, dtype: TF_DataType.TF_STRING);
if (input.rank == 0)
{
var parts = string_split_v2(array_ops.stack(new[] { input }),
sep: sep,
maxsplit: maxsplit,
name: name);
return parts;
}
var result = tf.Context.ExecuteOp("StringSplitV2", name,
new ExecuteOpArgs(input, sep)
{
GetGradientAttrs = op => new
{
maxsplit = op.get_attr <int>("maxsplit")
}
}.SetAttributes(new { maxsplit }));
var(indices, values, shape) = (result[0], result[1], result[2]);
indices.shape = new Shape(-1, 2);
values.shape = new Shape(-1);
shape.shape = new Shape(2);
var sparse_result = new SparseTensor(indices, values, shape);
return RaggedTensor.from_value_rowids(sparse_result.values,
value_rowids: sparse_result.indices[Slice.All, 0],
nrows: sparse_result.dense_shape[0],
validate: false);
}));
RaggedTensor _ragged_getitem_inner_dimensions(RaggedTensor input, Slice[] slices)
{
return(input);
}