How Many Parameters After the Flatten?

A small image classifier is fed a batch of colour photographs. The batch arrives as a 4-D tensor whose axes are (batch, height, width, channels):

$\text{input shape} = (32,\ 10,\ 10,\ 3)$

The network then runs the pipeline below.

• The flatten step leaves the batch axis untouched and merges every remaining axis into a single axis of length $d$.
• Each dense layer computes $\mathbf{Z} = \mathbf{A}W + \mathbf{1}\mathbf{b}^{\mathsf{T}}$, where $W$ has shape (inputs, outputs) and the bias vector $\mathbf{b}$ holds one bias per output unit.

How many learnable numbers does this network hold in total — every weight and every bias, across all three dense layers?

Your answer is an exact whole number.