# tensorplay.frombuffer

Source: https://www.tensorplay.cn/docs/generated/tensorplay.frombuffer.html

```python
tensorplay.frombuffer(buffer, *, dtype, count=-1, offset=0, requires_grad=False) → Tensor
```

Creates a 1-dimensional [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor) from an object that implements
the Python buffer protocol.

Skips the first offset bytes in the buffer, and interprets the rest of
the raw bytes as a 1-dimensional tensor of type [dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType) with count
elements.

Note that either of the following must be true:

1. count is a positive non-zero number, and the total number of bytes
in the buffer is more than offset plus count times the size
(in bytes) of [dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType).

2. count is negative, and the length (number of bytes) of the buffer
subtracted by the offset is a multiple of the size (in bytes) of
[dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType).

The returned tensor and buffer share the same memory. Modifications to
the tensor will be reflected in the buffer and vice versa. The returned
tensor is not resizable.

> **Note**
>
> This function increments the reference count for the object that owns the shared memory. Therefore, such memory will not be deallocated before the returned tensor goes out of scope.

> **Warning**
>
> This function’s behavior is undefined when passed an object implementing the buffer protocol whose data is not on the CPU. Doing so is likely to cause a segmentation fault.

> **Warning**
>
> This function does not try to infer the dtype (hence, it is not optional). Passing a different dtype than its source may result in unexpected behavior.

Parameters:

buffer ([object](https://docs.python.org/3/builtins/functions.html#object)) – a Python object that exposes the buffer interface.

Keyword Arguments:

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType)) – the desired data type of returned tensor.

- count ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – the number of desired elements to be read. If negative, all the elements (until the end of the buffer) will be read. Default: -1.

- offset ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – the number of bytes to skip at the start of the buffer. Default: 0.

- requires_grad ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

```
>>> import array
>>> a = array.array('i', [1, 2, 3])
>>> t = tensorplay.frombuffer(a, dtype=tensorplay.int32)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

>>> # Interprets the signed char bytes as 32-bit integers.
>>> # Each 4 signed char elements will be interpreted as
>>> # 1 signed 32-bit integer.
>>> import array
>>> a = array.array('b', [-1, 0, 0, 0])
>>> tensorplay.frombuffer(a, dtype=tensorplay.int32)
tensor([255], dtype=tensorplay.int32)
```