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# tensorflow::ops::DynamicStitch

`#include <data_flow_ops.h>`

Interleave the values from the `data` tensors into a single tensor.

## Summary

Builds a merged tensor such that

```    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
```

For example, if each `indices[m]` is scalar or vector, we have

```    # Scalar indices:
merged[indices[m], ...] = data[m][...]```

```    # Vector indices:
merged[indices[m][i], ...] = data[m][i, ...]
```

Each `data[i].shape` must start with the corresponding `indices[i].shape`, and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we must have `data[i].shape = indices[i].shape + constant`. In terms of this `constant`, the output shape is

```merged.shape = [max(indices)] + constant
```

Values are merged in order, so if an index appears in both `indices[m][i]` and `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the merged result. If you do not need this guarantee, ParallelDynamicStitch might perform better on some devices.

For example:

```    indices[0] = 6
indices[1] = [4, 1]
indices[2] = [[5, 2], [0, 3]]
data[0] = [61, 62]
data[1] = [[41, 42], [11, 12]]
data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
[51, 52], [61, 62]]
```

This method can be used to merge partitions created by `dynamic_partition` as illustrated on the following example:

```    # Apply function (increments x_i) on elements for which a certain condition
# apply (x_i != -1 in this example).
x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
partitioned_data = tf.dynamic_partition(
partitioned_data[1] = partitioned_data[1] + 1.0
condition_indices = tf.dynamic_partition(
x = tf.dynamic_stitch(condition_indices, partitioned_data)
# Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
# unchanged.
```

Arguments:

Returns:

• `Output`: The merged tensor.

### Constructors and Destructors

`DynamicStitch(const ::tensorflow::Scope & scope, ::tensorflow::InputList indices, ::tensorflow::InputList data)`

### Public attributes

`merged`
`::tensorflow::Output`
`operation`
`Operation`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
``` ```
``` ```
`operator::tensorflow::Output() const `
``` ```
``` ```

## Public attributes

### merged

`::tensorflow::Output merged`

### operation

`Operation operation`

## Public functions

### DynamicStitch

``` DynamicStitch(
const ::tensorflow::Scope & scope,
::tensorflow::InputList indices,
::tensorflow::InputList data
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `
[{ "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" }]