# tf.dynamic_stitch(indices, data, name=None)

### tf.dynamic_stitch(indices, data, name=None)

See the guide: Tensor Transformations > Slicing and Joining

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

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.

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]]


#### Args:

• indices: A list of at least 1 Tensor objects of type int32.
• data: A list with the same number of Tensor objects as indices of Tensor objects of the same type.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as data.

Defined in tensorflow/python/ops/gen_data_flow_ops.py.