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Mojo function

gather_nd

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

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[StaticConstantOrigin]): The target architecture to execute on.

Args:

data (TileTensor[dtype, address_space=data.address_space, linear_idx_type=data.linear_idx_type, element_size=data.element_size]): Tensor of rank data_rank >= 1.
indices (TileTensor[indices_type, address_space=indices.address_space, linear_idx_type=indices.linear_idx_type, element_size=indices.element_size]): 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[dtype, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size]): Tensor of rank data_rank + indices_rank - indices_shape[-1] - 1 - b.
ctx (DeviceContext): The device context as prepared by the graph compiler.