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

# Aliases:

• tf.compat.v1.parallel_stack
• tf.compat.v2.parallel_stack

 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, CA, B, C); the output tensor will have the shape (N, A, B, CA, 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])

# Args:

• values: A list of Tensor objects with the same shape and type.
• name: A name for this operation (optional).

# Returns:

• output: A stacked Tensor with the same type as values.