fix for copy_stochastic with Dtensor

[ethansmith2000]

some ops are not yet supported for Dtensor
This seems to work, though there may be better solutions.

def copy_stochastic_(target: Tensor, source: Tensor):
    """
    copies source into target using stochastic rounding

    Args:
        target: the target tensor with dtype=bfloat16
        source: the target tensor with dtype=float32
    """
    if isinstance(target, DTensor):
        target_for_op = target.to_local()
    else:
        target_for_op = target
    if isinstance(source, DTensor):
        source_for_op = source.to_local()
    else:
        source_for_op = source

    # create a random 16 bit integer
    result = torch.randint_like(
        source_for_op,
        dtype=torch.int32,
        low=0,
        high=(1 << 16),
    )

    # add the random number to the lower 16 bit of the mantissa
    result.add_(source_for_op.view(dtype=torch.int32))

    # mask off the lower 16 bit of the mantissa
    result.bitwise_and_(-65536)  # -65536 = FFFF0000 as a signed int32

    # copy the higher 16 bit into the target tensor
    target_for_op.copy_(result.view(dtype=torch.float32))

    torch.distributed.breakpoint(0)

    if isinstance(target, DTensor):
        target_for_op = DTensor.from_local(target_for_op, device_mesh=target.device_mesh, placements=target.placements, shape=target.shape, stride=target.stride())
        target.copy_(target_for_op)
    #     del target_for_op
    # if isinstance(source, DTensor):
    #     del source_for_op

    del result
    ```