# Function Differences with torch.flatten

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## torch.flatten

```python
torch.flatten(
    input,
    start_dim=0,
    end_dim=-1
)
```

For more information, see [torch.flatten](https://pytorch.org/docs/1.8.1/generated/torch.flatten.html).

## mindspore.ops.flatten

```python
mindspore.ops.flatten(input, order='C', *, start_dim=1, end_dim=-1)
```

For more information,
see [mindspore.ops.flatten](https://mindspore.cn/docs/en/r2.0/api_python/ops/mindspore.ops.flatten.html).

## Differences

PyTorch: Supports the flatten operation of elements by specified dimensions, where `start_dim` defaults to 0 and `end_dim` defaults to -1.

MindSpore：Supports the flatten operation of elements by specified dimensions, where `start_dim` defaults to 1 and `end_dim` defaults to -1. Prioritizes row or column flatten by `order` to "C" or "F".

| Categories | Subcategories | PyTorch   | MindSpore | Differences                                                   |
|------------|---------------|-----------|-----------|---------------------------------------------------------------|
| Parameter  | Parameter 1   | input     | input     | Same function                                                 |
|            | Parameter 2   | -         | order     | Flatten order, PyTorch does not have this Parameter           |
|            | Parameter 3   | start_dim | start_dim | Same function                                                 |
|            | Parameter 4   | end_dim   | end_dim   | Same function                                                 |

## Code Example

```python
import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# MindSpore
input_tensor = ms.Tensor(np.ones(shape=[1, 2, 3, 4]), ms.float32)
output = ops.flatten(input_tensor)
print(output.shape)
# Out：
# (1, 24)

input_tensor = ms.Tensor(np.ones(shape=[1, 2, 3, 4]), ms.float32)
output = ops.flatten(input_tensor, start_dim=2)
print(output.shape)
# Out：
# (1, 2, 12)

# PyTorch
input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
output1 = torch.flatten(input=input_tensor, start_dim=1)
print(output1.shape)
# Out：
# torch.Size([1, 24])

input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
output2 = torch.flatten(input=input_tensor, start_dim=2)
print(output2.shape)
# Out：
# torch.Size([1, 2, 12])
```