# W3cubDocs

/TensorFlow 2.4

This operation pads `input` according to the `paddings` and `constant_values` you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many padding values to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many padding values to add after the contents of `input` in that dimension. `constant_values` is a scalar tensor of the same type as `input` that indicates the value to use for padding `input`.

The padded size of each dimension D of the output is:

`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`

#### For example:

```# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# 'constant_values' is 0
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 0]
[0, 0, 2, 2, 0, 0]
[0, 0, 0, 0, 0, 0]]
```
Args
`input` A `Tensor`.
`paddings` A `Tensor`. Must be one of the following types: `int32`, `int64`.
`constant_values` A `Tensor`. Must have the same type as `input`.
`name` A name for the operation (optional).
Returns
A `Tensor`. Has the same type as `input`.