# Path: init2winit/model_lib/base_model.py
# def _evaluate_batch(flax_module, params, batch_stats, batch, metrics_bundle,
#                     apply_one_hot_in_loss):
# def _predict_batch(flax_module,
#                    params,
#                    batch_stats,
#                    batch,
#                    output_activation_fn=None):
#   def __init__(self, hps, dataset_meta_data, loss_name, metrics_name):
#   def evaluate_batch(self, params, batch_stats, batch):
#   def predict_batch(self,
#                     params,
#                     batch_stats,
#                     batch,
#                     apply_output_activation_fn=False):
#   def training_cost(self, params, batch, batch_stats=None, dropout_rng=None):
#   def get_fake_batch(self, hps):
#   def build_flax_module(self):
# class BaseModel(object):
# 
# Path: init2winit/model_lib/model_utils.py
# ACTIVATIONS = {
#     'relu': jax.nn.relu,
#     'id': lambda x: x,
#     'tanh': jnp.tanh,
#     'sigmoid': jax.nn.sigmoid,
#     'silu': jax.nn.silu,
#     'leaky_relu': jax.nn.leaky_relu,
# }
# INITIALIZERS = {
#     'delta_orthogonal': initializers.delta_orthogonal,
#     'orthogonal': initializers.orthogonal,
#     'lecun_normal': lecun_normal,
#     'xavier_uniform': initializers.xavier_uniform,
# }
# class ScalarMultiply(nn.Module):
#   def __call__(self, x):
# def get_normalizer(normalizer,
#                    train,
#                    batch_size=None,
#                    virtual_batch_size=None,
#                    total_batch_size=None,
#                    dtype=jnp.float32,
#                    data_format='NHWC'):
#     def identity_wrapper(*args, **kwargs):
#       def identity(x, *args, **kwargs):
# def apply_label_smoothing(one_hot_targets, label_smoothing):
# def sync_batchnorm_stats(state):
# def cross_device_avg(pytree):
# def l2_regularization(params, l2_decay_rank_threshold):
# def flatten_dict(nested_dict, sep='/'):
# def rescale_layers(params, layer_rescale_factors):


import functools
import math
import jax.numpy as jnp
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from ml_collections.config_dict import config_dict
    y = maybe_normalize()(x)
    y = nn.relu(y)
    y = conv(features=4 * self.growth_rate, kernel_size=(1, 1), name='conv1')(y)

    y = maybe_normalize()(y)
    y = nn.relu(y)
    y = conv(
        features=self.growth_rate,
        kernel_size=(3, 3),
        padding=((1, 1), (1, 1)),
        name='conv2')(y)

    # Concatenate the output and input along the features dimension.
    y = jnp.concatenate([y, x], axis=3)
    return y


class TransitionBlock(nn.Module):
  """Block that implements downsampling between dense layers.

  Downsampling is achieved by a 1x1 convoluationl layer (with the associated
  batch norm and ReLU) and a 2x2 average pooling layer.
  """
  num_features: int
  dtype: model_utils.Dtype = jnp.float32
  normalizer: str = 'batch_norm'

  @nn.compact
  def __call__(self, x, train):
    conv = functools.partial(nn.Conv, use_bias=False, dtype=self.dtype)

# Path: init2winit/model_lib/base_model.py
# def _evaluate_batch(flax_module, params, batch_stats, batch, metrics_bundle,
#                     apply_one_hot_in_loss):
# def _predict_batch(flax_module,
#                    params,
#                    batch_stats,
#                    batch,
#                    output_activation_fn=None):
#   def __init__(self, hps, dataset_meta_data, loss_name, metrics_name):
#   def evaluate_batch(self, params, batch_stats, batch):
#   def predict_batch(self,
#                     params,
#                     batch_stats,
#                     batch,
#                     apply_output_activation_fn=False):
#   def training_cost(self, params, batch, batch_stats=None, dropout_rng=None):
#   def get_fake_batch(self, hps):
#   def build_flax_module(self):
# class BaseModel(object):
# 
# Path: init2winit/model_lib/model_utils.py
# ACTIVATIONS = {
#     'relu': jax.nn.relu,
#     'id': lambda x: x,
#     'tanh': jnp.tanh,
#     'sigmoid': jax.nn.sigmoid,
#     'silu': jax.nn.silu,
#     'leaky_relu': jax.nn.leaky_relu,
# }
# INITIALIZERS = {
#     'delta_orthogonal': initializers.delta_orthogonal,
#     'orthogonal': initializers.orthogonal,
#     'lecun_normal': lecun_normal,
#     'xavier_uniform': initializers.xavier_uniform,
# }
# class ScalarMultiply(nn.Module):
#   def __call__(self, x):
# def get_normalizer(normalizer,
#                    train,
#                    batch_size=None,
#                    virtual_batch_size=None,
#                    total_batch_size=None,
#                    dtype=jnp.float32,
#                    data_format='NHWC'):
#     def identity_wrapper(*args, **kwargs):
#       def identity(x, *args, **kwargs):
# def apply_label_smoothing(one_hot_targets, label_smoothing):
# def sync_batchnorm_stats(state):
# def cross_device_avg(pytree):
# def l2_regularization(params, l2_decay_rank_threshold):
# def flatten_dict(nested_dict, sep='/'):
# def rescale_layers(params, layer_rescale_factors):


# Path: init2winit/model_lib/adabelief_densenet.py
import functools
import math
import jax.numpy as jnp
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from ml_collections.config_dict import config_dict

    y = maybe_normalize()(x)
    y = nn.relu(y)
    y = conv(features=4 * self.growth_rate, kernel_size=(1, 1), name='conv1')(y)

    y = maybe_normalize()(y)
    y = nn.relu(y)
    y = conv(
        features=self.growth_rate,
        kernel_size=(3, 3),
        padding=((1, 1), (1, 1)),
        name='conv2')(y)

    # Concatenate the output and input along the features dimension.
    y = jnp.concatenate([y, x], axis=3)
    return y


class TransitionBlock(nn.Module):
  """Block that implements downsampling between dense layers.

  Downsampling is achieved by a 1x1 convoluationl layer (with the associated
  batch norm and ReLU) and a 2x2 average pooling layer.
  """
  num_features: int
  dtype: model_utils.Dtype = jnp.float32
  normalizer: str = 'batch_norm'

  @nn.compact
  def __call__(self, x, train):
    conv = functools.partial(nn.Conv, use_bias=False, dtype=self.dtype)

    maybe_normalize = model_utils.get_normalizer(self.normalizer, train)