human
Solve the following python programming problem given the following code:
# Create the initial parameters
def get_params(vocab_size, num_hiddens, init_rng):
    num_inputs = num_outputs = vocab_size

    def normal(shape, rng):
        return jax.random.normal(rng, shape=shape) * 0.01

    hidden_rng, out_rng = jax.random.split(init_rng)
    # Hidden layer parameters
    W_xh = normal((num_inputs, num_hiddens), hidden_rng)
    W_hh = normal((num_hiddens, num_hiddens), hidden_rng)
    b_h = jnp.zeros(num_hiddens)
    # Output layer parameters
    W_hq = normal((num_hiddens, num_outputs), out_rng)
    b_q = jnp.zeros(num_outputs)
    params = [W_xh, W_hh, b_h, W_hq, b_q]
    return params

# Create the initial state
# We assume this is a tuple of one element (later we will use longer tuples)
def init_rnn_state(batch_size, num_hiddens):
    return (jnp.zeros((batch_size, num_hiddens)),)

# Forward function.
# Input sequence is (T,B,V), where T is length of the sequence, B is batch size, V is vocab size.
# We iterate over each time step, and process the batch (for that timestep in parallel).
# Output sequence is (T*B, V), since we concatenate all the time steps.
# We also return the final state, so we can process the next subsequence.
@jax.jit
def rnn(params, state, inputs):
    # Here `inputs` shape: (`num_steps`, `batch_size`, `vocab_size`)
    W_xh, W_hh, b_h, W_hq, b_q = params
    (H,) = state
    outputs = []
    # Shape of `X`: (`batch_size`, `vocab_size`)
    for X in inputs:
        H = jnp.tanh((X @ W_xh) + (H @ W_hh) + b_h)
        Y = H @ W_hq + b_q
        outputs.append(Y)
    return jnp.concatenate(outputs, axis=0), (H,)

# Make the model class
# Input X to apply is (B,T) matrix of integers (from vocab encoding).
# We transpose this to (T,B) then one-hot encode to (T,B,V), where V is vocab.
# The result is passed to the forward function.
# (We define the forward function as an argument, so we can change it later.)

class RNNModelScratch:
    """A RNN Model implemented from scratch."""

    def __init__(self, vocab_size, num_hiddens, get_params, init_state, forward_fn):
...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Model
We fit an unconditional RNN, for language modeling (ie. not vec2seq or seq2seq. Following the D2L notation, the model has the form
$$\begin{align}
H_t &= \phi(X_t W_{xh} + H_{t-1}  W_{hh} + b_h) \
O_t &= H_t W_{hq} + b_q
\end{align}
$$
where $X_t$ is the $(n,d)$ matrix of (one-hot) inputs 
(for batch size $n$ and vocabulary size $d$),
$H_t$ is the $(n,h)$ matrix of hidden states 
(for $h$ hidden states),
and $O_t$ is the $(n,q)$ matrix of output logits
(for $q$ output labels, often $q=d$).