i have following code :
input_shape = (72, 6427)

inputs = Input(shape=(input_shape,))


x = Dense(6427, activation='relu', input_shape=(input_shape,)) (inputs)
x = Dropout(0.25) (x)
x = Dense(3200, activation='relu') (x)
x = Dropout(0.20) (x)
x = Dense(1800, activation='relu') (x)
x = Dropout(0.15) (x)
x = Dense(1024, activation='relu') (x)
x = Dropout(0.10) (x)
x = Dense(512, activation='relu') (x)
x = Dropout(0.05) (x)
x = Dense(256, activation='relu') (x)
x = Dense(128, activation='relu') (x)
x = Dense(64, activation='relu') (x)
x = Dense(32, activation='relu')  (x)



# Defining three separate outputs

out_high_1d = Dense(1, name='high_output_1d')(x)  # No activation, linear output
out_low_1d = Dense(1, name='low_output_1d')(x)    # No activation, linear output
out_priority_1d = Dense(1, activation='sigmoid', name='priority_output_1d')(x)

out_high_2d = Dense(1, name='high_output_2d')(x)  # No activation, linear output
out_low_2d = Dense(1, name='low_output_2d')(x)    # No activation, linear output
out_priority_2d = Dense(1, activation='sigmoid', name='priority_output_2d')(x)

out_high_3d = Dense(1, name='high_output_3d')(x)  # No activation, linear output
out_low_3d = Dense(1, name='low_output_3d')(x)    # No activation, linear output
out_priority_3d = Dense(1, activation='sigmoid', name='priority_output_3d')(x)

out_high_5d = Dense(1, name='high_output_5d')(x)  # No activation, linear output
out_low_5d = Dense(1, name='low_output_5d')(x)    # No activation, linear output
out_priority_5d = Dense(1, activation='sigmoid', name='priority_output_5d')(x)

# Constructing the model
model = Model(inputs=inputs, outputs=[
    out_high_1d, out_low_1d, out_priority_1d,out_high_2d, out_low_2d, out_priority_2d,
    out_high_3d, out_low_3d, out_priority_3d,out_high_5d, out_low_5d, out_priority_5d])

model.compile(optimizer='adam',
              loss={
                'high_output_1d': 'mse', 'low_output_1d': 'mse', 'priority_output_1d': 'binary_crossentropy',
                'high_output_2d': 'mse', 'low_output_2d': 'mse', 'priority_output_2d': 'binary_crossentropy',
                'high_output_3d': 'mse', 'low_output_3d': 'mse', 'priority_output_3d': 'binary_crossentropy',
                'high_output_5d': 'mse', 'low_output_5d': 'mse', 'priority_output_5d': 'binary_crossentropy'
              },
              metrics={
                'high_output_1d': ['mae'], 'low_output_1d': ['mae'], 'priority_output_1d': ['accuracy'],
                'high_output_2d': ['mae'], 'low_output_2d': ['mae'], 'priority_output_2d': ['accuracy'],
                'high_output_3d': ['mae'], 'low_output_3d': ['mae'], 'priority_output_3d': ['accuracy'],
                'high_output_5d': ['mae'], 'low_output_5d': ['mae'], 'priority_output_5d': ['accuracy']
              },
              loss_weights={
                'high_output_1d': 1.0, 'low_output_1d': 1.0, 'priority_output_1d': 1.0,
                'high_output_2d': 1.0, 'low_output_2d': 1.0, 'priority_output_2d': 1.0,
                'high_output_3d': 1.0, 'low_output_3d': 1.0, 'priority_output_3d': 1.0,
                'high_output_5d': 1.0, 'low_output_5d': 1.0, 'priority_output_5d': 1.0
              }
              )

and im getting this error:
{
	"name": "ValueError",
	"message": "Invalid dtype: tuple",
	"stack": "---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[8], line 3
      1 input_shape = (70, 6427)
----> 3 inputs = Input(shape=(input_shape,))
      6 x = Dense(6427, activation='relu', input_shape=(input_shape,)) (inputs)
      7 x = Dropout(0.25) (x)

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:143, in Input(shape, batch_size, dtype, sparse, batch_shape, name, tensor)
     89 @keras_export([\"keras.layers.Input\", \"keras.Input\"])
     90 def Input(
     91     shape=None,
   (...)
     97     tensor=None,
     98 ):
     99     \"\"\"Used to instantiate a Keras tensor.
    100 
    101     A Keras tensor is a symbolic tensor-like object, which we augment with
   (...)
    141     ```
    142     \"\"\"
--> 143     layer = InputLayer(
    144         shape=shape,
    145         batch_size=batch_size,
    146         dtype=dtype,
    147         sparse=sparse,
    148         batch_shape=batch_shape,
    149         name=name,
    150         input_tensor=tensor,
    151     )
    152     return layer.output

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:46, in InputLayer.__init__(self, shape, batch_size, dtype, sparse, batch_shape, input_tensor, name, **kwargs)
     43     raise ValueError(\"You must pass a `shape` argument.\")
     45 if shape is not None:
---> 46     shape = backend.standardize_shape(shape)
     47     batch_shape = (batch_size,) + shape
     48 self.batch_shape = tuple(batch_shape)

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\common\\variables.py:523, in standardize_shape(shape)
    520 if config.backend() == \"jax\" and \"_DimExpr\" in str(type(e)):
    521     # JAX2TF tracing uses JAX-native dimension expressions
    522     continue
--> 523 if not is_int_dtype(type(e)):
    524     raise ValueError(
    525         f\"Cannot convert '{shape}' to a shape. \"
    526         f\"Found invalid entry '{e}' of type '{type(e)}'. \"
    527     )
    528 if e < 0:

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\common\\variables.py:554, in is_int_dtype(dtype)
    552 @keras_export(\"keras.backend.is_int_dtype\")
    553 def is_int_dtype(dtype):
--> 554     dtype = standardize_dtype(dtype)
    555     return dtype.startswith(\"int\") or dtype.startswith(\"uint\")

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\common\\variables.py:495, in standardize_dtype(dtype)
    492     dtype = dtype.__name__
    494 if dtype not in dtypes.ALLOWED_DTYPES:
--> 495     raise ValueError(f\"Invalid dtype: {dtype}\")
    496 return dtype

ValueError: Invalid dtype: tuple"
}