In Python, you can reshape arrays using various libraries such as NumPy and TensorFlow. Here, I’ll provide examples of reshaping arrays using both NumPy and TensorFlow.

Reshaping Arrays with NumPy: NumPy provides the `reshape`

function to reshape arrays. Here’s an example:

```
import numpy as np
# Create a 1D array
arr = np.array([1, 2, 3, 4, 5, 6])
# Reshape the array to a 2D array with 2 rows and 3 columns
reshaped_arr = arr.reshape(2, 3)
print(reshaped_arr)
```

The output will be:

```
[[1 2 3]
[4 5 6]]
```

In this example, we create a 1D array `arr`

with 6 elements. Then, we use the `reshape`

function to reshape it into a 2D array with 2 rows and 3 columns. The resulting reshaped array `reshaped_arr`

is printed.

Reshaping Arrays with TensorFlow: If you’re working with TensorFlow, you can use the `tf.reshape`

function to reshape arrays. Here’s an example:

```
import tensorflow as tf
# Create a 1D tensor
tensor = tf.constant([1, 2, 3, 4, 5, 6])
# Reshape the tensor to a 2D tensor with 2 rows and 3 columns
reshaped_tensor = tf.reshape(tensor, (2, 3))
print(reshaped_tensor.numpy())
```

The output will be:

```
[[1 2 3]
[4 5 6]]
```

In this example, we create a 1D tensor `tensor`

using TensorFlow’s `constant`

function. Then, we use the `tf.reshape`

function to reshape it into a 2D tensor with 2 rows and 3 columns. Finally, we convert the reshaped tensor to a NumPy array using `numpy()`

and print it.

Both NumPy and TensorFlow allow you to reshape arrays into different shapes as long as the total number of elements remains the same. Keep in mind that reshaping can create a new view of the original array/tensor, so modifications to one may affect the other.

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