/TensorFlow 2.4


Concatenates a list of SparseTensor along the specified dimension.

Concatenation is with respect to the dense versions of these sparse tensors. It is assumed that each input is a SparseTensor whose elements are ordered along increasing dimension number.

All inputs' shapes must match, except for the concat dimension. The indices, values, and shapes lists must have the same length.

The output shape is identical to the inputs', except along the concat dimension, where it is the sum of the inputs' sizes along that dimension.

The output elements will be resorted to preserve the sort order along increasing dimension number.

This op runs in O(M log M) time, where M is the total number of non-empty values across all inputs. This is due to the need for an internal sort in order to concatenate efficiently across an arbitrary dimension.

For example, if concat_dim = 1 and the inputs are

sp_inputs[0]: shape = [2, 3]
[0, 2]: "a"
[1, 0]: "b"
[1, 1]: "c"

sp_inputs[1]: shape = [2, 4]
[0, 1]: "d"
[0, 2]: "e"

then the output will be

shape = [2, 7]
[0, 2]: "a"
[0, 4]: "d"
[0, 5]: "e"
[1, 0]: "b"
[1, 1]: "c"

Graphically this is equivalent to doing

[    a] concat [  d e  ] = [    a   d e  ]
[b c  ]        [       ]   [b c          ]
indices A list of at least 2 Tensor objects with type int64. 2-D. Indices of each input SparseTensor.
values A list with the same length as indices of Tensor objects with the same type. 1-D. Non-empty values of each SparseTensor.
shapes A list with the same length as indices of Tensor objects with type int64. 1-D. Shapes of each SparseTensor.
concat_dim An int. Dimension to concatenate along. Must be in range [-rank, rank), where rank is the number of dimensions in each input SparseTensor.
name A name for the operation (optional).
A tuple of Tensor objects (output_indices, output_values, output_shape).
output_indices A Tensor of type int64.
output_values A Tensor. Has the same type as values.
output_shape A Tensor of type int64.

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