Questions about a multivariate timeseries forecasting model - keras

I have trouble understanding the model I’m trying to create.
I have few questions so I’ll explain my model first and what I’m trying to do:

I have created sequences of data (input and ouput of the model) 7 timesteps each so the input would be the values of the days of a certain week and the output is the values of the days the following week (**so input1 of my model has an imput shape of (7,1) same as the output**).
I also have prepared another input list that has some extra features like holiday flag and weather condition for the **following week** so the model’s second input2 has the shape (7,7) each example.

This is the full model summary:

After the lstm layers and a fully connected NN, I tried to concatenate the two inputs together, basically, I want to concatenate input2 to the output of the layer dense_1.
So I’m concatenating (7,1) shape with the second input ‘input_2‘ of shape (7,7)

My questions:

1- The outputs of my dense layers confuse me I thought they would be in the shape (None, number of units) but they seem to be (None, 7 , number of units) there is always the 2nd dimension "7" which i don’t understand.

2- For the concatenation part, since i’m adding a 7×7 input to a 7×1 input i was expecting to have 49+7=56 units connected to the next dense layer but the number of parameters tells me it’s not the case being just 14*120+120=1800

3- I was thinking of having 7 units in the last layer ( 1 for each day to predict ) but had to make it 1 so it would output (7,1) and for it to work, there is something i’m clearly missing.

This is the code for model definition:

input1 = tf.keras.layers.Input(shape=(7,1),name="input_1")
x = tf.keras.layers.Conv1D(120, kernel_size=5,strides=1,activation="relu",
                           padding="causal",input_shape=[7, 1])(input1)
x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(210, return_sequences=True),name="LSTM_1")(x)
x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(210, return_sequences=True,name="LSTM_2"))(x)
x = tf.keras.layers.Dense(60,activation="relu",name="dense_1_1")(x)
x = tf.keras.layers.Dense(30,activation="relu",name="dense_1_2")(x)
x = tf.keras.layers.Dense(7,name="dense_1_3")(x)

input2 = tf.keras.layers.Input(shape=(7,7),name="input_2")
concat = tf.keras.layers.concatenate([x, input2],name="concat_1")
x = tf.keras.layers.Dense(120,activation="selu",name="dense_2_1")(concat)
x = tf.keras.layers.Dense(90,activation="selu",name="dense_2_2")(x)
x = tf.keras.layers.Dense(60,activation="selu",name="dense_2_3")(x)
output = tf.keras.layers.Dense(1)(x)

model = tf.keras.Model([input1, input2], output)

These are the dimensions of the inputs (input1 & input2) and the output