Questions about a multivariate timeseries forecasting model - keras
From Keras docs:
https://keras.io/api/layers/core_layers/dense/

Input shape
N-D tensor with shape: (batch_size, ..., input_dim). The most common
situation would be a 2D input with shape (batch_size, input_dim).
Output shape
N-D tensor with shape: (batch_size, ..., units). For instance, for a
2D input with shape (batch_size, input_dim), the output would have
shape (batch_size, units).
If the input to the layer has a rank greater than 2, then Dense
computes the dot product between the inputs and the kernel along the
last axis of the inputs and axis 1 of the kernel (using tf.tensordot).
For example, if input has dimensions (batch_size, d0, d1), then we
create a kernel with shape (d1, units), and the kernel operates along
axis 2 of the input, on every sub-tensor of shape (1, 1, d1) (there
are batch_size * d0 such sub-tensors). The output in this case will
have shape (batch_size, d0, units).

The input to dense_1_1 is (None, 7, 420). The Dense layer will be applied to every [:,i,:] for in 1..7 slice of the input producing an output of size (None, 7, 60)
If you're expecting an output with shape (None, 60) you can reshape the input to (None, 420*7) but that would not make sense in this case since you would want to apply the Dense layer to each time slice of your input.