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class WeightedLayer(Layer):
  def __init__(self, n_input, n_memb, **kwargs):
    super(WeightedLayer, self).__init__( **kwargs)
    self.n = n_input   # 16 features
    self.m = n_memb    # 3

  def build(self, batch_input_shape):
    super(WeightedLayer, self).build(batch_input_shape)

  def call(self, input_):
    CP = []
    self.batch_size = tf.shape(input_)[0]
    for batch in tf.range(self.batch_size):
      xd_shape = [self.m]
      c_shape = [1]
      cp= input_[batch,0,:]
      for d in range(1,self.n):
        c_shape.insert(0,self.m)
        xd_shape.insert(0,1)
        xd = tf.reshape(input_[batch,d,:], (xd_shape))
        c = tf.reshape(cp,(c_shape))
        cp = tf.matmul(c , xd)
      flat_cp = tf.reshape(cp,(1, self.m**self.n))
      CP.append(flat_cp)
    return tf.reshape(tf.stack(CP), (self.batch_size, self.m**self.n))

  def compute_output_shape(self, batch_input_shape):
    return tf.TensorShape([self.batch_size, self.m ** self.n])

X_train = np.random.uniform(0, 1, (200, 16, 3))
X_test = np.random.uniform(0, 1, (200, 16, 3))
y_train = np.random.uniform(0, 1, (200,))
y_test = np.random.uniform(0, 1, (200,))

Model = keras.models.Sequential()
Model.add(WeightedLayer(n_input=16, n_memb=3,input_shape=(16, 3)))
Model.compile(loss='mean_squared_error', optimizer='adam')
Model.fit(X_train, y_train,epochs=20,batch_size=10,validation_data=(X_test, y_test))

      /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
      693       except Exception as e:  # pylint:disable=broad-except
      694         if hasattr(e, 'ag_error_metadata'):
--> 695           raise e.ag_error_metadata.to_exception(e)
      696         else:
      697           raise

InaccessibleTensorError: in user code:

<ipython-input-66-5c6ffada5c7a>:29 call  *
 return tf.reshape(tf.stack(CP), (self.batch_size, self.m**self.n))
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper  **
 return target(*args, **kwargs)
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:1424 stack
 return gen_array_ops.pack(values, axis=axis, name=name)
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py:6401 pack
 "Pack", values=values, axis=axis, name=name)
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:750 _apply_op_helper
 attrs=attr_protos, op_def=op_def)
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py:597 _create_op_internal
 inp = self.capture(inp)
 /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py:647 capture
 % (tensor, tensor.graph, self))

InaccessibleTensorError: The tensor 'Tensor("weighted_layer_37/while/Reshape_30:0", shape=(1, 43046721), 
dtype=float32)' cannot be accessed here: it is defined in another function or code block. Use return values, explicit 
Python locals or TensorFlow collections to access it. Defined in: FuncGraph(name=weighted_layer_37_while_body_44082, 
id=139893449741456); accessed from: FuncGraph(name=weighted_layer_37_scratch_graph, id=139893452012688).

class WeightedLayer(Layer):
 def __init__(self, n_input, n_memb, **kwargs):
    super(WeightedLayer, self).__init__( **kwargs)
    self.n = n_input   # 16 features
    self.m = n_memb    # 3 
    self.batch_size = None

 def build(self, batch_input_shape):
    #self.batch_size = batch_input_shape[0]
    self.batch_size = tf.shape(batch_input_shape)[0]
    super(WeightedLayer, self).build(batch_input_shape)
    
    
    
 def call(self, input_):
    CP = []
    for batch in range(self.batch_size):
        xd_shape = [self.m]
        c_shape = [1]
        cp = input_[batch,0,:]
        for d in range(1,self.n):
            c_shape.insert(0,self.m)
            xd_shape.insert(0,1)
            xd = tf.reshape(input_[batch,d,:], (xd_shape))
            c = tf.reshape(cp,(c_shape))
            cp = tf.matmul(c , xd)

        flat_cp = tf.reshape(cp,(1, self.m**self.n))
        CP.append(flat_cp)

    return tf.reshape(tf.stack(CP), (self.batch_size, self.m**self.n))

 def compute_output_shape(self,batch_input_shape):
  return tf.TensorShape([self.batch_size, self.m** self.n])