diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
index 414556d24..51ceac0f2 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
@@ -779,74 +779,6 @@ def greedy_search_batch(
         )
 
 
-def deprecated_greedy_search_batch_for_cross_attn(
-    model: nn.Module, encoder_out: torch.Tensor
-) -> List[List[int]]:
-    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
-    Args:
-      model:
-        The transducer model.
-      encoder_out:
-        Output from the encoder. Its shape is (N, T, C), where N >= 1.
-    Returns:
-      Return a list-of-list of token IDs containing the decoded results.
-      len(ans) equals to encoder_out.size(0).
-    """
-    assert encoder_out.ndim == 3
-    assert encoder_out.size(0) >= 1, encoder_out.size(0)
-
-    device = next(model.parameters()).device
-
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
-
-    blank_id = model.decoder.blank_id
-    unk_id = getattr(model, "unk_id", blank_id)
-    context_size = model.decoder.context_size
-
-    hyps = [[blank_id] * context_size for _ in range(batch_size)]
-
-    decoder_input = torch.tensor(
-        hyps,
-        device=device,
-        dtype=torch.int64,
-    )  # (batch_size, context_size)
-
-    decoder_out = model.decoder(decoder_input, need_pad=False)
-    decoder_out = model.joiner.decoder_proj(decoder_out)
-    encoder_out = model.joiner.encoder_proj(encoder_out)
-
-    # decoder_out: (batch_size, 1, decoder_out_dim)
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
-        # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
-        logits = model.joiner(
-            current_encoder_out, decoder_out.unsqueeze(1), project_input=False
-        )
-        # logits'shape (batch_size, 1, 1, vocab_size)
-        logits = logits.squeeze(1).squeeze(1)  # (batch_size, vocab_size)
-        assert logits.ndim == 2, logits.shape
-        y = logits.argmax(dim=1).tolist()
-        emitted = False
-        for i, v in enumerate(y):
-            if v not in (blank_id, unk_id):
-                hyps[i].append(v)
-                emitted = True
-        if emitted:
-            # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps]
-            decoder_input = torch.tensor(
-                decoder_input,
-                device=device,
-                dtype=torch.int64,
-            )
-            decoder_out = model.decoder(decoder_input, need_pad=False)
-            decoder_out = model.joiner.decoder_proj(decoder_out)
-
-    ans = [h[context_size:] for h in hyps]
-    return ans
-
-
 def deprecated_greedy_search_batch_for_cross_attn(
     model: nn.Module,
     encoder_out: torch.Tensor,