# tf.dynamic_stitch

``````dynamic_stitch(
indices,
data,
name=None
)``````

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

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

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

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

``````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]]``````

``````# 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.``````

#### ARGS：

• indices：至少有一个 int32 类型的张量对象的列表.
• data：与相同类型的张量对象的索引长度相同的列表.
• name：操作的名称(可选).

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