**ReLU (Rectified Linear Activation) - tf.nn.relu**:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.nn.relu(x)
print(output.numpy())
```

**Output**:

```
[0. 0. 0. 1. 2.]
```

**Sigmoid - tf.math.sigmoid**:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.math.sigmoid(x)
print(output.numpy())
```

**Output**:

```
[0.11920292 0.26894143 0.5 0.7310586 0.8807971 ]
```

**Hyperbolic Tangent (tanh) - tf.math.tanh**:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.math.tanh(x)
print(output.numpy())
```

**Output**:

```
[-0.9640276 -0.7615942 0. 0.7615942 0.9640276]
```

```
[-0.9640276 -0.7615942 0. 0.7615942 0.9640276]
```

It indicates the values of the tanh function applied to each element of the tensor x.

**Softmax - tf.nn.softmax**:

```
x = tf.constant([2.0, 1.0, 0.1], dtype=tf.float32)
output = tf.nn.softmax(x)
print(output.numpy())
```

**Output**:

```
[0.6590012 0.24243297 0.09856585]
```

```
[0.6590011 0.24243297 0.09856592]
```

It indicates the normalized probabilities computed by the softmax function for each element in the tensor x.

**Leaky ReLU - tf.nn.leaky_relu**:

Example:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
alpha = 0.2
output = tf.nn.leaky_relu(x, alpha)
print(output.numpy())
```

**Output**:

```
[-0.4 -0.2 0. 1. 2. ]
```

**ELU (Exponential Linear Unit) - tf.nn.elu**:

Example:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.nn.elu(x)
print(output.numpy())
```

**Output**:

```
[-0.86466473 -0.63212055 0. 1. 2. ]
```

**Softplus - tf.nn.softplus**:

Example:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.nn.softplus(x)
print(output.numpy())
```

**Output**:

```
[0.126928 0.3132617 0.6931472 1.3132616 2.126928 ]
```

**Swish - Custom Activation**:

Example:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = x * tf.nn.sigmoid(x)
print(output.numpy())
```

**Output**:

```
[-0.10499359 -0.2689414 0. 0.7310586 1.716472 ]
```

**Hard Sigmoid - tf.nn.hard_sigmoid**:

Example:

```
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=tf.float32)
output = tf.nn.hard_sigmoid(x)
print(output.numpy())
```

**Output**:

```
[0.2 0.4 0.5 0.6 0.8]
```

These are some common tensor activation functions in TensorFlow along with examples and their expected outputs. Activation functions are crucial components in neural networks that introduce non-linearity, enabling the modeling of complex relationships in data.

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