PyTorch torch.allclose() method is used to** “check if all elements of two tensors are approximately equal within some tolerance.”** It helps verify if two tensors are** “close enough**” in value, especially in unit tests or checking the correctness of computations in numerical methods where rounding errors might occur.

**Syntax**

`torch.allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False)`

**Parameters**

**input (Tensor)**: The first tensor to compare.

**other (Tensor)**: The second tensor to compare. It should have the same shape as the input.

**rtol (float, optional)**: The relative tolerance.**atol (float, optional)**: The absolute tolerance.**equal_nan (bool, optional)**: Whether to treat NaNs equally if they appear in the same location in both tensors.

**Example 1: How to Use torch.allclose() method**

```
import torch
a = torch.tensor([1.0, 2.0, 3.0])
b = torch.tensor([1.0, 2.0, 3.0001])
print(torch.allclose(a, b))
```

**Output**

`False`

**Example 2: Demonstrating the effect of rtol and atol**

```
import torch
a = torch.tensor([1.0, 2.0, 3.0])
b = torch.tensor([1.0, 2.0, 3.1])
# Using default tolerances
print(torch.allclose(a, b)) # Expected: False
# Increasing the relative tolerance
print(torch.allclose(a, b, rtol=0.05))
```

**Output**

```
False
True
```

**Example 3: Handling NaN values**

```
import torch
a = torch.tensor([1.0, 2.0, float('nan')])
b = torch.tensor([1.0, 2.0, float('nan')])
# Without setting equal_nan=True
print(torch.allclose(a, b))
# Setting equal_nan=True
print(torch.allclose(a, b, equal_nan=True))
```

**Output**

```
False
True
```

That’s it!

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Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Machine Learning frameworks like PyTorch and Tensorflow is a testament to his versatility and commitment to the craft.