ttnn.logical_and_

ttnn.logical_and_ = Operation(python_fully_qualified_name='ttnn.logical_and_', function=<ttnn._ttnn.operations.binary.logical_and__t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Computes inplace logical AND of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\mathrm{{input\_tensor\_a}}_i \& \mathrm{{input\_tensor\_b}}_i\]

Parameters:

input_tensor_a (ttnn.Tensor) – the input tensor.
input_tensor_b (ttnn.Tensor) – the input tensor.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16, BFLOAT8_B	TILE	2, 3, 4

Example

>>> tensor1 = ttnn.from_torch(torch.tensor([[2, 2], [2, 2]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 1], [1, 1]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> ttnn.logical_and_(tensor1, tensor2)