Project: s41467-024-51477-5 | Function: __call__ (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Core forward pass implementation of the BayesianNeuralField1D model combining all components: input scaling, seasonal/Fourier features, learnable activation combinations, and hierarchical Bayesian neural network structure as described in Box 1

**KEY TERMS:** {
  "log_scale_adjustment": "covariate scale factors ξ^0",
  "scaled_x": "scaled covariates e^{ξ_i^0} x_i(s,t)",
  "activation_weight": "activation combination weights γ^ℓ",
  "activation_fn": "convex combination of u_j^ℓ activations",
  "layer_scale": "layer-wise scale factors",
  "h": "hidden layer activations h^ℓ"
}

**CONTEXT CODE:**         tfd.Normal(predictions, 0.01 + jnp.exp(log_noise_scale)), 1
    )

  elif LikelihoodDist(distribution) == LikelihoodDist.NB:
    treedef = jax.tree_util.tree_structure(mlp_template)
    mlp_params = jax.tree_util.tree_unflatten(treedef, params[3:])
    predictions = mlp.apply(mlp_params, x)
    mean = jax.nn.softplus(predictions)
    shape = jax.nn.softplus(params[1])

    negative_binomial = tfd.NegativeBinomial(
        total_count=1 / shape, logits=-jnp.log(shape) - jnp.log(mean)
    )
    return tfd.Independent(negative_binomial, 1)

  elif LikelihoodDist(distribution) == LikelihoodDist.ZINB:
    treedef = jax.tree_util.tree_structure(mlp_template)
    mlp_params = jax.tree_util.tree_unflatten(treedef, params[3:])
    predictions = mlp.apply(mlp_params, x)
    mean = jax.nn.softplus(predictions)
    shape = jax.nn.softplus(params[1])
    inflated_loc_probs = 1 / (1 + jnp.exp(-params[2]))

    zero_inflated_negative_binomial = tfd.ZeroInflatedNegativeBinomial(
        total_count=1 / shape,
        logits=-jnp.log(shape) - jnp.log(mean),
        inflated_loc_probs=inflated_loc_probs * jnp.ones(shape=mean.shape),
    )
    return tfd.Independent(zero_inflated_negative_binomial, 1)

  else:
    raise AssertionError('Unknown likelihood distribution:', distribution)


class BayesianNeuralField1D(nn.Module):
  """Linen Module implementing a 1D Bayesian neural field."""

  width: int
  depth: int
  input_scales: np.ndarray
  fourier_degrees: np.ndarray
  interactions: np.ndarray
  num_seasonal_harmonics: np.ndarray = flax.struct.field(
      default_factory=lambda: np.zeros((0,))
  )
  seasonality_periods: np.ndarray = flax.struct.field(
      default_factory=lambda: np.zeros((0,))
  )

  @nn.compact
  def __call__(self, x):
    """Core forward pass implementation of the BayesianNeuralField1D model."""
    # MASKED: Core forward pass implementation of the BayesianNeuralField1D model combining all components: input scaling, seasonal/Fourier features, learnable activation combinations, and hierarchical Bayesian neural network structure as described in Box 1
    raise NotImplementedError("Function has been masked for testing purposes")


Complete the function implementation replacing "raise NotImplementedError".