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Mojo function

slice_shape

def slice_shape[input_type: DType, start_type: DType, stop_type: DType, step_type: DType](input_buf: TileTensor[input_type, Storage=input_buf.Storage, address_space=input_buf.address_space, linear_idx_type=input_buf.linear_idx_type], start_buf: TileTensor[start_type, Storage=start_buf.Storage, address_space=start_buf.address_space, linear_idx_type=start_buf.linear_idx_type], stop_buf: TileTensor[stop_type, Storage=stop_buf.Storage, address_space=stop_buf.address_space, linear_idx_type=stop_buf.linear_idx_type], step_buf: TileTensor[step_type, Storage=step_buf.Storage, address_space=step_buf.address_space, linear_idx_type=step_buf.linear_idx_type]) -> IndexList[input_buf.LayoutType.rank]

Computes the shape that results from slicing input_buf with the given start, stop, and step tensors.

Validates that the index tensors each have one entry per rank of input_buf and that no step is zero, then normalizes and clamps the indices per axis to determine the output extent along each dimension.

Args:

input_buf (TileTensor[input_type, Storage=input_buf.Storage, address_space=input_buf.address_space, linear_idx_type=input_buf.linear_idx_type]): Source tensor whose slice shape is computed.
start_buf (TileTensor[start_type, Storage=start_buf.Storage, address_space=start_buf.address_space, linear_idx_type=start_buf.linear_idx_type]): One-dimensional tensor of starting indices, one per rank.
stop_buf (TileTensor[stop_type, Storage=stop_buf.Storage, address_space=stop_buf.address_space, linear_idx_type=stop_buf.linear_idx_type]): One-dimensional tensor of stopping indices (exclusive), one per rank.
step_buf (TileTensor[step_type, Storage=step_buf.Storage, address_space=step_buf.address_space, linear_idx_type=step_buf.linear_idx_type]): One-dimensional tensor of strides, one per rank; each must be non-zero.

Returns:

IndexList[input_buf.LayoutType.rank]: An IndexList holding the extent of each dimension after slicing.