 There was no error when converting the lite model but an error occurred when calling the Interpreter allocate_tensors() method. It will appear if the Conv1D data_format parameter is set to channels_first and the dilation_rate parameter > 1
@383156098 Could you please try to upgrade to the latset TF version as you are using an older version which is not actively supported. If your application allows it, kindly consider changing the data_format parameter in your Conv1D layer to channels_last. This is the more common format for TFLite and might avoid the allocation issue.

Thank you! import tensorflow as tf

# Define the Conv1D layer with 'channels_last'
conv1d = tf.keras.layers.Conv1D(data_format="channels_last",
                                filters=128,
                                kernel_size=3,
                                strides=1,
                                padding="same",
                                groups=128,
                                dilation_rate=2)
# Build the model
model = tf.keras.Sequential([conv1d])
model.build(input_shape=[1, 2000, 128])  # Update input shape accordingly
model.summary()

# Convert to TFLite model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,
    tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()

# Save the TFLite model
with open('conv_tas_net.tflite', 'wb') as wb:
    wb.write(tflite_model)

# Load and allocate tensors with TFLite interpreter
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

print("Model loaded and tensors allocated successfully.")
 This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.
### 1. System information

- Windows 10 and Centos 7
- Tensorflow 2.10.0 ~ 2.10.1 

### 2. Code

    conv1d = tf.keras.layers.Conv1D(data_format="channels_first",
                                    filters=128,
                                    kernel_size=3,
                                    strides=1,
                                    padding="same",
                                    groups=128,
                                    dilation_rate=2)
    model = tf.keras.Sequential(conv1d)
    model.build(input_shape=[1, 128, 2000])
    model.summary()
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.experimental_new_converter = True
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
        tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
    ]
    tflite_model = converter.convert()
    with open('../tflite_checkpoint/conv_tas_net.tflite', 'wb') as wb:
        wb.write(tflite_model)

    interpreter = tf.lite.Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

### 5. error logs
    interpreter.allocate_tensors()
  File "G:\anaconda3\envs\bragi\lib\site-packages\tensorflow\lite\python\interpreter.py", line 513, in allocate_tensors
    return self._interpreter.AllocateTensors()
RuntimeError: tensorflow/lite/kernels/space_to_batch_nd.cc:67 op_context->block_shape->dims->data[0] != spatial_dims_num (2 != 1)Node number 0 (SPACE_TO_BATCH_ND) failed to prepare.Failed to apply the default TensorFlow Lite delegate indexed at 0.

