A **tensor** is a data structure that represents matrices and vectors. It can be thought of as a multi-dimensional array. 

The **`unique` function** in TensorFlow is used to find unique values in a tensor.
It takes the original tensor `x`. Then, it returns two tensors: 
1. `y` 
2.  `idx`

Tensor `y` contains the unique values from the original tensor. Tensor `idx` contains the index position where each unique value is found.

> Tensor `y` has the same order of elements as in Tensor `x`. 

> Tensor `idx` has the same number of elements as in tensor `x`.

The illustration below shows how the `unique` function works in TensorFlow:

# Syntax

The syntax of the `unique` function is as follows:

```python
tf.unique(x, out_idx=tf.dtypes.int32, name=None)
```

# Parameters

The `unique` function has three parameters:

* `x`: A one-dimensional tensor.
* `out_idx`: Data type of the return tensor. Possible values include `tf.int32` and `tf.int64`. It is `tf.int32` by default.
* `name`: A name for the operation.

> Only the first parameter is compulsory. The rest are optional.

# Return values

The `unique` function returns two tensors: `y` and `idx`. 

Tensor `y` contains the unique values from the original tensor. Tensor `idx` contains the index position where each unique value is found.

# Example

The code snippet below shows how the `unique` function is used in TensorFlow:

import tensorflow as tf

# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
x = tf.constant([1,1,2,4,4,4,7,8,8])
y, idx = tf.unique(x)
print("x:", x) # => [1, 1, 2, 4, 4, 4, 7, 8, 8]
print("y:", y) # => [1, 2, 4, 7, 8]
print("idx", idx) # => [0, 0, 1, 2, 2, 2, 3, 4, 4]

What is the unique function in TensorFlow?

A tensor is a data structure that represents matrices and vectors. It can be thought of as a multi-dimensional array. The unique function in TensorFlow is used to find unique values in a tensor. It takes the original tensor x. Then, it returns two tensors:  y idx  Tensor y contains the unique values from the original tensor. Tensor idx contains the index position where each unique value is found.  Tensor y has the same order of elements as in Tensor x.   Tensor idx has the same number of elements as in tensor x.  The illustration below shows how the unique function works in TensorFlow: 