def METHOD_NAME(conv_op, input_dim, sample_inchannel):
    sample_seq_len = 200
    sample_bsz = 10
    x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
    # N x C x H x W
    # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
    x = conv_op(x)
    # N x C x H x W
    x = x.transpose(1, 2)
    # N x H x C x W
    bsz, seq = x.size()[:2]
    per_channel_dim = x.size()[3]
    # bsz: N, seq: H, CxW the rest
    return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim