# tf.dynamic_partition(data, partitions, num_partitions, name=None)

### tf.dynamic_partition(data, partitions, num_partitions, name=None)

See the guide: Tensor Transformations > Slicing and Joining

Partitions data into num_partitions tensors using indices from partitions.

For each index tuple js of size partitions.ndim, the slice data[js, ...] becomes part of outputs[partitions[js]]. The slices with partitions[js] = i are placed in outputs[i] in lexicographic order of js, and the first dimension of outputs[i] is the number of entries in partitions equal to i. In detail,

    outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]

outputs[i] = pack([data[js, ...] for js if partitions[js] == i])


data.shape must start with partitions.shape.

For example:

    # Scalar partitions.
partitions = 1
num_partitions = 2
data = [10, 20]
outputs[0] = []  # Empty with shape [0, 2]
outputs[1] = [[10, 20]]

# Vector partitions.
partitions = [0, 0, 1, 1, 0]
num_partitions = 2
data = [10, 20, 30, 40, 50]
outputs[0] = [10, 20, 50]
outputs[1] = [30, 40]


#### Args:

• data: A Tensor.
• partitions: A Tensor of type int32. Any shape. Indices in the range [0, num_partitions).
• num_partitions: An int that is >= 1. The number of partitions to output.
• name: A name for the operation (optional).

#### Returns:

A list of num_partitions Tensor objects of the same type as data.

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