Mojo function

gather_nd

gather_nd[dtype: DType, indices_type: DType, batch_dims: Int, target: StringSlice[StaticConstantOrigin] = "cpu", single_thread_blocking_override: Bool = False](data: LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], indices: LayoutTensor[indices_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], output: LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], ctx: DeviceContextPtr)

GatherND operation as defined in https://github.com/onnx/onnx/blob/main/docs/Operators.md#GatherND. Based on reference implementation: https://github.com/onnx/onnx/blob/main/onnx/backend/test/case/node/gathernd.py.

Parameters:

dtype (DType): Type of data tensor.
indices_type (DType): Type of indices tensor.
batch_dims (Int): Number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:].
target (StringSlice): The target architecture to execute on.
single_thread_blocking_override (Bool): If True, then the operation is run synchronously using a single thread.

Args:

data (LayoutTensor): Tensor of rank data_rank >= 1.
indices (LayoutTensor): Tensor of rank indices_rank >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
output (LayoutTensor): Tensor of rank data_rank + indices_rank - indices_shape[-1] - 1 - b.
ctx (DeviceContextPtr): The DeviceContextPtr as prepared by the graph compiler.