Mojo function

gather_nd

gather_nd[dtype: DType, indices_type: DType, batch_dims: Int, target: StringSlice[StaticConstantOrigin] = StringSlice("cpu")](data: TileTensor[dtype, data.LayoutType, data.origin, address_space=data.address_space, linear_idx_type=data.linear_idx_type, element_size=data.element_size], indices: TileTensor[indices_type, indices.LayoutType, indices.origin, address_space=indices.address_space, linear_idx_type=indices.linear_idx_type, element_size=indices.element_size], output: TileTensor[dtype, output.LayoutType, output.origin, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size], 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.

Args:

data (TileTensor): Tensor of rank data_rank >= 1.
indices (TileTensor): 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 (TileTensor): Tensor of rank data_rank + indices_rank - indices_shape[-1] - 1 - b.
ctx (DeviceContextPtr): The DeviceContextPtr as prepared by the graph compiler.