ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 35, 30) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 35) dtype=float32>]

[1170300423]

No matter which way I choose to write it, it pops up this error
############ the first way #################
history = model.fit(
trainX,
trainY,
epochs=100,
batch_size=60,
shuffle=True,
verbose=0,
validation_data=(testX, testY)
)
############ the second way #################
history = model.fit(
trainX,
trainY,
epochs=100,
batch_size=60,
shuffle=True,
verbose=0,
validation_data=[testX, testY]
)

############# error message ######################

ValueError Traceback (most recent call last)
in
6 shuffle=True,
7 verbose=0,
----> 8 validation_data=(testX, testY)
9 )

~/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1224 use_multiprocessing=use_multiprocessing,
1225 return_dict=True,
-> 1226 use_cached_eval_dataset=True)
1227 val_logs = {'val' + name: val for name, val in val_logs.items()}
1228 epoch_logs.update(val_logs)

~/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)
1499 with tf.profiler.experimental.Trace('test', step_num=step, _r=1):
1500 callbacks.on_test_batch_begin(step)
-> 1501 tmp_logs = self.test_function(iterator)
1502 if data_handler.should_sync:
1503 context.async_wait()

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in call(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
922 # In this case we have not created variables on the first call. So we can
923 # run the first trace but we should fail if variables are created.
--> 924 results = self._stateful_fn(*args, **kwds)
925 if self._created_variables and not ALLOW_DYNAMIC_VARIABLE_CREATION:
926 raise ValueError("Creating variables on a non-first call to a function"

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, *args, **kwargs)
3036 with self._lock:
3037 (graph_function,
-> 3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3458 call_context_key in self._function_cache.missed):
3459 return self._define_function_with_shape_relaxation(
-> 3460 args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3461
3462 self._function_cache.missed.add(call_context_key)

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _define_function_with_shape_relaxation(self, args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3380
3381 graph_function = self._create_graph_function(
-> 3382 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3383 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
3384

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3306 arg_names=arg_names,
3307 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3308 capture_by_value=self._capture_by_value),
3309 self._function_attributes,
3310 function_spec=self.function_spec,

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: func_outputs contains only Tensors, CompositeTensors,

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().wrapped(*args, **kwds)
669 return out
670

~/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise

ValueError: in user code:

/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:1330 test_function  *
    return step_function(self, iterator)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:1320 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
    return fn(*args, **kwargs)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:1313 run_step  **
    outputs = model.test_step(data)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:1267 test_step
    y_pred = self(x, training=False)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py:1020 __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
/home/wf/Program_file/anaconda3/lib/python3.7/site-packages/keras/engine/input_spec.py:202 assert_input_compatibility
    ' input tensors. Inputs received: ' + str(inputs))

ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 35, 30) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 35) dtype=float32>]

please can you give me some advise