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op {
graph_op_name: "SparseSplit"
in_arg {
name: "split_dim"
description: <<END
0-D. The dimension along which to split. Must be in the range
`[0, rank(shape))`.
END
}
in_arg {
name: "indices"
description: <<END
2-D tensor represents the indices of the sparse tensor.
END
}
in_arg {
name: "values"
description: <<END
1-D tensor represents the values of the sparse tensor.
END
}
in_arg {
name: "shape"
description: <<END
1-D. tensor represents the shape of the sparse tensor.
output indices: A list of 1-D tensors represents the indices of the output
sparse tensors.
END
}
out_arg {
name: "output_values"
description: <<END
A list of 1-D tensors represents the values of the output sparse
tensors.
END
}
out_arg {
name: "output_shape"
description: <<END
A list of 1-D tensors represents the shape of the output sparse
tensors.
END
}
attr {
name: "num_split"
description: <<END
The number of ways to split.
END
}
summary: "Split a `SparseTensor` into `num_split` tensors along one dimension."
description: <<END
If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
`[0 : shape[split_dim] % num_split]` gets one extra dimension.
For example, if `split_dim = 1` and `num_split = 2` and the input is
input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are:
output_tensor[0] = shape = [2, 4]
[ a ]
[b c ]
output_tensor[1] = shape = [2, 3]
[ d e ]
[ ]
END
}
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