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``mx.symbol.broadcast_to``
====================================================
Description
----------------------
Broadcasts the input array to a new shape.
Broadcasting is a mechanism that allows NDArrays to perform arithmetic operations
with arrays of different shapes efficiently without creating multiple copies of arrays.
Also see, `Broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_ for more explanation.
Broadcasting is allowed on axes with size 1, such as from `(2,1,3,1)` to
`(2,8,3,9)`. Elements will be duplicated on the broadcasted axes.
For example::
broadcast_to([[1,2,3]], shape=(2,3)) = [[ 1., 2., 3.],
[ 1., 2., 3.]])
The dimension which you do not want to change can also be kept as `0` which means copy the original value.
So with `shape=(2,0)`, we will obtain the same result as in the above example.
Usage
----------
.. code:: r
mx.symbol.broadcast_to(...)
Arguments
------------------
+----------------------------------------+------------------------------------------------------------+
| Argument | Description |
+========================================+============================================================+
| ``data`` | NDArray-or-Symbol. |
| | |
| | The input |
+----------------------------------------+------------------------------------------------------------+
| ``shape`` | Shape(tuple), optional, default=[]. |
| | |
| | The shape of the desired array. We can set the dim to zero |
| | if it's same as the original. E.g `A = broadcast_to(B, |
| | shape=(10, 0, 0))` has the same meaning as `A = |
| | broadcast_axis(B, axis=0, |
| | size=10)`. |
+----------------------------------------+------------------------------------------------------------+
| ``name`` | string, optional. |
| | |
| | Name of the resulting symbol. |
+----------------------------------------+------------------------------------------------------------+
Value
----------
``out`` The result mx.symbol
Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/tensor/broadcast_reduce_op_value.cc#L82