# InvalidArgumentError: Cannot batch ragged tensors with different number of rows

- **ID:** `tensorflow/ragged-tensor-batch-size-mismatch`
- **Domain:** tensorflow
- **Category:** data_error
- **Verification:** ai_generated
- **Fix Rate:** 85%

## Root Cause

Attempting to batch multiple RaggedTensors that have different row lengths (first dimension) in a way that requires uniform batch size, e.g., using tf.data.Dataset.batch() without padding.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow 2.9 | active | — | — |
| tensorflow 2.10 | active | — | — |
| tensorflow 2.11 | active | — | — |

## Workarounds

1. **Use padded_batch() instead of batch() on the dataset: dataset = dataset.padded_batch(batch_size, padded_shapes=[None, None])** (90% success)
   ```
   Use padded_batch() instead of batch() on the dataset: dataset = dataset.padded_batch(batch_size, padded_shapes=[None, None])
   ```
2. **Pad RaggedTensors manually before batching using ragged_tensor.to_tensor(default_value=0, shape=[None, max_len]) then use regular batch().** (85% success)
   ```
   Pad RaggedTensors manually before batching using ragged_tensor.to_tensor(default_value=0, shape=[None, max_len]) then use regular batch().
   ```

## Dead Ends

- **** — drop_remainder only controls whether the last incomplete batch is dropped, but the error occurs within a batch where rows are already unequal. (90% fail)
- **** — While it resolves the batching error, it can cause OOM if ragged dimensions are large; also it changes semantics. (75% fail)