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TensorFlow has functions to solve other more complex tasks. For example, we will use a mathematical operator that calculates the derivative of `y` with respect to its expression `x` parameter. For this purpose, we use the `tf.gradients()` function.

Let us consider the math function `y = 2x²`. We want to compute the gradient `di y` with respect to `x=1`. The following is the code to compute this gradient:

1. First, import the TensorFlow library:
```    import TensorFlow as tf
```
2. The `x` variable is the independent variable of the function:
```    x = tf.placeholder(tf.float32)
```
3. Let's build the function:
```    y =  2*x*x
```
4. Finally, we call the ` tf.gradients()` function with `y` and `x` as arguments:
```    var_grad = tf.gradients(y, x)
```
5. To evaluate the gradient, we must build a session: ...

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