tf.raw_ops.ReadVariableXlaSplitND
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Splits resource variable input tensor across all dimensions.
tf.raw_ops.ReadVariableXlaSplitND(
resource, T, N, num_splits, paddings=[], name=None
)
An op which splits the resource variable input tensor based on the given
num_splits attribute, pads slices optionally, and returned the slices. Slices
are returned in row-major order.
This op may be generated via the TPU bridge.
For example, with input tensor:
[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]
num_splits:
[2, 2]
and paddings:
[1, 1]
the expected outputs is:
[[0, 1],
[3, 4]]
[[2, 0],
[5, 0]]
[[6, 7],
[0, 0]]
[[8, 0],
[0, 0]]
Args |
|---|
resource
|
A Tensor of type resource.
Resource variable of input tensor to split across all dimensions.
}
out_arg {
name: "outputs"
description: <
|
T
|
A tf.DType.
|
N
|
An int that is >= 1.
|
num_splits
|
A list of ints.
Number of ways to split per dimension. Shape dimensions must be evenly
divisible.
|
paddings
|
An optional list of ints. Defaults to [].
Optional list of right paddings per dimension of input tensor to apply before
splitting. This can be used to make a dimension evenly divisible.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A list of N Tensor objects with type T.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.ReadVariableXlaSplitND\n\n\u003cbr /\u003e\n\nSplits resource variable input tensor across all dimensions.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.ReadVariableXlaSplitND`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ReadVariableXlaSplitND)\n\n\u003cbr /\u003e\n\n tf.raw_ops.ReadVariableXlaSplitND(\n resource, T, N, num_splits, paddings=[], name=None\n )\n\nAn op which splits the resource variable input tensor based on the given\nnum_splits attribute, pads slices optionally, and returned the slices. Slices\nare returned in row-major order.\n\nThis op may be generated via the TPU bridge.\n\nFor example, with `input` tensor: \n\n [[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]]\n\n`num_splits`: \n\n [2, 2]\n\nand `paddings`: \n\n [1, 1]\n\nthe expected `outputs` is: \n\n [[0, 1],\n [3, 4]]\n [[2, 0],\n [5, 0]]\n [[6, 7],\n [0, 0]]\n [[8, 0],\n [0, 0]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `resource` | A `Tensor` of type `resource`. Resource variable of input tensor to split across all dimensions. } out_arg { name: \"outputs\" description: \\\u003c |\n| `T` | A [`tf.DType`](../../tf/dtypes/DType). |\n| `N` | An `int` that is `\u003e= 1`. |\n| `num_splits` | A list of `ints`. Number of ways to split per dimension. Shape dimensions must be evenly divisible. |\n| `paddings` | An optional list of `ints`. Defaults to `[]`. Optional list of right paddings per dimension of input tensor to apply before splitting. This can be used to make a dimension evenly divisible. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A list of `N` `Tensor` objects with type `T`. ||\n\n\u003cbr /\u003e"]]
