r"""Reshapes a tensor without changing its data. Args: inp: input tensor to reshape. target_shape: target shape compatible with the original shape. One shape dimension is allowed to be `-1` . When a shape dimension is `-1` , the corresponding output tensor shape di
(inp: Tensor, target_shape: Iterable[int])
| 994 | |
| 995 | |
| 996 | def reshape(inp: Tensor, target_shape: Iterable[int]) -> Tensor: |
| 997 | r"""Reshapes a tensor without changing its data. |
| 998 | |
| 999 | Args: |
| 1000 | inp: input tensor to reshape. |
| 1001 | target_shape: target shape compatible with the original shape. One shape dimension is allowed |
| 1002 | to be `-1` . When a shape dimension is `-1` , the corresponding output tensor shape dimension |
| 1003 | must be inferred from the length of the tensor and the remaining dimensions. |
| 1004 | |
| 1005 | Returns: |
| 1006 | an output tensor having the same data type, elements, and underlying element order as `inp` . |
| 1007 | |
| 1008 | Examples: |
| 1009 | >>> x = F.arange(12) |
| 1010 | >>> x |
| 1011 | Tensor([ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.], device=xpux:0) |
| 1012 | >>> F.reshape(x, (3, 4)) |
| 1013 | Tensor([[ 0. 1. 2. 3.] |
| 1014 | [ 4. 5. 6. 7.] |
| 1015 | [ 8. 9. 10. 11.]], device=xpux:0) |
| 1016 | >>> F.reshape(x, (2, -1)) |
| 1017 | Tensor([[ 0. 1. 2. 3. 4. 5.] |
| 1018 | [ 6. 7. 8. 9. 10. 11.]], device=xpux:0) |
| 1019 | """ |
| 1020 | return inp.reshape(target_shape) |
| 1021 | |
| 1022 | |
| 1023 | def flatten(inp: Tensor, start_axis: int = 0, end_axis: int = -1) -> Tensor: |
no test coverage detected