[Relay] Create a zero tensor of batch size

Questions
#1

I want to create a tensor of batch_size when coverting a custom op into tvm. Below is what I’ve done

import tvm.relay.op as _op
from tvm.relay.frontend.common import infer_shape
boxes_num = relay.var("boxes_num", shape=(relay.Any(),), dtype=dtype)
batch_size = infer_shape(boxes_num)[0]
ids = _op.zeros(shape=(batch_size,), dtype=dtype)

But tvm says 『Expected Array[IntImm], but got Array[index 0: tir.Any]』 It seems that zeros do not handle dynamic shape.

Then I looked after the usage of zeros in tvm/python/tvm/relay/frontend/pytorch.py like the snippet below

        X_shape = _infer_shape(X)  # (seq_num, batch, feature_size)

        hidden_size = _infer_shape(_weights[0])[0] / 4
        batch_size = X_shape[1]

        # Initialize hidden states if not provided.
        layers_h = []
        layers_c = []
        hidden_layers_num = num_directions * num_layers
        if h_0 is None:
            if has_proj:
                h_0 = _op.zeros((batch_size, proj_size), X_dtype)
            else:
                h_0 = _op.zeros((batch_size, hidden_size), X_dtype)
            for i in range(hidden_layers_num):
                layers_h.append(h_0)

So the current zeros should support dynamic shape according to the above. Or do I miss anything?