# tf.dynamic_stitch

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. 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(