# tf.parallel_stack

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/array_ops.py#L1376-L1435) |

Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor in parallel.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.parallel_stack`](https://www.tensorflow.org/api_docs/python/tf/parallel_stack)

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    tf.parallel_stack(
        values, name='parallel_stack'
    )

Requires that the shape of inputs be known at graph construction time.

Packs the list of tensors in `values` into a tensor with rank one higher than
each tensor in `values`, by packing them along the first dimension.
Given a list of length `N` of tensors of shape `(A, B, C)`; the `output`
tensor will have the shape `(N, A, B, C)`.

#### For example:

    x = tf.constant([1, 4])
    y = tf.constant([2, 5])
    z = tf.constant([3, 6])
    tf.parallel_stack([x, y, z])  # [[1, 4], [2, 5], [3, 6]]

The difference between `stack` and `parallel_stack` is that `stack` requires
all the inputs be computed before the operation will begin but doesn't require
that the input shapes be known during graph construction.

`parallel_stack` will copy pieces of the input into the output as they become
available, in some situations this can provide a performance benefit.

Unlike `stack`, `parallel_stack` does NOT support backpropagation.

This is the opposite of unstack. The numpy equivalent is

    tf.parallel_stack([x, y, z]) = np.asarray([x, y, z])

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| ## Args ||
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| `values` | A list of `Tensor` objects with the same shape and type. |
| `name` | A name for this operation (optional). |

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| ## Returns ||
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| `output` | A stacked `Tensor` with the same type as `values`. |

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| ## Raises ||
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| `RuntimeError` | if executed in eager mode. |

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## eager compatibility

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parallel_stack is not compatible with eager execution.

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