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op {
graph_op_name: "ReduceJoin"
in_arg {
name: "inputs"
description: <<END
The input to be joined. All reduced indices must have non-zero size.
END
}
in_arg {
name: "reduction_indices"
description: <<END
The dimensions to reduce over. Dimensions are reduced in the
order specified. Omitting `reduction_indices` is equivalent to passing
`[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported.
END
}
out_arg {
name: "output"
description: <<END
Has shape equal to that of the input with reduced dimensions removed or
set to `1` depending on `keep_dims`.
END
}
attr {
name: "keep_dims"
description: <<END
If `True`, retain reduced dimensions with length `1`.
END
}
attr {
name: "separator"
description: <<END
The separator to use when joining.
END
}
summary: "Joins a string Tensor across the given dimensions."
description: <<END
Computes the string join across dimensions in the given string Tensor of shape
`[\\(d_0, d_1, ..., d_{n-1}\\)]`. Returns a new Tensor created by joining the input
strings with the given separator (default: empty string). Negative indices are
counted backwards from the end, with `-1` being equivalent to `n - 1`. If
indices are not specified, joins across all dimensions beginning from `n - 1`
through `0`.
For example:
```python
# tensor `a` is [["a", "b"], ["c", "d"]]
tf.reduce_join(a, 0) ==> ["ac", "bd"]
tf.reduce_join(a, 1) ==> ["ab", "cd"]
tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
tf.reduce_join(a, [0, 1]) ==> "acbd"
tf.reduce_join(a, [1, 0]) ==> "abcd"
tf.reduce_join(a, []) ==> [["a", "b"], ["c", "d"]]
tf.reduce_join(a) = tf.reduce_join(a, [1, 0]) ==> "abcd"
```
END
}
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