generate_creative_birds.py [75:91]:
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def evaluate_in_chunks(max_batch_size, model, *args):
    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
    chunked_outputs = [model(*i) for i in split_args]
    if len(chunked_outputs) == 1:
        return chunked_outputs[0]
    return torch.cat(chunked_outputs, dim=0)

def evaluate_in_chunks_unet(max_batch_size, model, map_feats, *args):
    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
    split_map_feats = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), map_feats))))
    chunked_outputs = [model(*i, j) for i, j in zip(split_args, split_map_feats)]
    if len(chunked_outputs) == 1:
        return chunked_outputs[0]
    return torch.cat(chunked_outputs, dim=0)

def styles_def_to_tensor(styles_def):
    return torch.cat([t[:, None, :].expand(-1, n, -1) for t, n in styles_def], dim=1)
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part_generator.py [121:137]:
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def evaluate_in_chunks(max_batch_size, model, *args):
    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
    chunked_outputs = [model(*i) for i in split_args]
    if len(chunked_outputs) == 1:
        return chunked_outputs[0]
    return torch.cat(chunked_outputs, dim=0)

def evaluate_in_chunks_unet(max_batch_size, model, map_feats, *args):
    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
    split_map_feats = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), map_feats))))
    chunked_outputs = [model(*i, j) for i, j in zip(split_args, split_map_feats)]
    if len(chunked_outputs) == 1:
        return chunked_outputs[0]
    return torch.cat(chunked_outputs, dim=0)

def styles_def_to_tensor(styles_def):
    return torch.cat([t[:, None, :].expand(-1, n, -1) for t, n in styles_def], dim=1)
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