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# tf.raw_ops.Unpack

Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.

Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. For example, given a tensor of shape `(A, B, C, D)`;

If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`. (Note that the dimension unpacked along is gone, unlike `split`).

If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`. Etc.

This is the opposite of `pack`.

`value` A `Tensor`. 1-D or higher, with `axis` dimension size equal to `num`.
`num` An `int` that is `>= 0`.
`axis` An optional `int`. Defaults to `0`. Dimension along which to unpack. Negative values wrap around, so the valid range is `[-R, R)`.
`name` A name for the operation (optional).

A list of `num` `Tensor` objects with the same type as `value`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]