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op {
graph_op_name: "RaggedGather"
visibility: HIDDEN
in_arg {
name: "params_nested_splits"
description: <<END
The `nested_row_splits` tensors that define the row-partitioning for the
`params` RaggedTensor input.
END
}
in_arg {
name: "params_dense_values"
description: <<END
The `inner_values` for the `params` RaggedTensor. There was a terminology change
at the python level from dense_values to inner_values, so dense_values is the
deprecated name.
END
}
in_arg {
name: "indices"
description: <<END
Indices in the outermost dimension of `params` of the values that should be
gathered.
END
}
out_arg {
name: "output_nested_splits"
description: <<END
The `nested_row_splits` tensors that define the row-partitioning for the
returned RaggedTensor.
END
}
out_arg {
name: "output_dense_values"
description: "The `inner_values` for the returned RaggedTensor."
}
attr {
name: "PARAMS_RAGGED_RANK"
description: <<END
The ragged rank of the `params` RaggedTensor. `params_nested_splits` should
contain this number of `row_splits` tensors. This value should equal
`params.ragged_rank`.
END
}
attr {
name: "OUTPUT_RAGGED_RANK"
description: <<END
The ragged rank of the output RaggedTensor. `output_nested_splits` will contain
this number of `row_splits` tensors. This value should equal
`indices.shape.ndims + params.ragged_rank - 1`.
END
}
summary: <<END
Gather ragged slices from `params` axis `0` according to `indices`.
END
description: <<END
Outputs a `RaggedTensor` output composed from `output_dense_values` and
`output_nested_splits`, such that:
```python
output.shape = indices.shape + params.shape[1:]
output.ragged_rank = indices.shape.ndims + params.ragged_rank
output[i...j, d0...dn] = params[indices[i...j], d0...dn]
```
where
* `params =
ragged.from_nested_row_splits(params_dense_values, params_nested_splits)`
provides the values that should be gathered.
* `indices` ia a dense tensor with dtype `int32` or `int64`, indicating which
values should be gathered.
* `output =
ragged.from_nested_row_splits(output_dense_values, output_nested_splits)`
is the output tensor.
(Note: This c++ op is used to implement the higher-level python
`tf.ragged.gather` op, which also supports ragged indices.)
END
}
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