code: def _build_nn_model(
      input_dim,
      num_samples,
      num_classes=None,
      modeltype="Regression",
      batch_size=32,
      learning_rate=0.001,
      activation="relu",
      num_hidden_layers=2,
      verbose=0,
  ):
      # Define activation and regularization
      initial_lr = 0.001
      dropout_rate = 0.2  ### this is just a placeholder for now
      l1_reg = 0.1 * initial_lr
      l2_reg = 0.1 * initial_lr
      regularization = l1_l2(l1=l1_reg, l2=l2_reg)
  
      ### You need to over-ride the num classes in two cases ###
      if modeltype == "Binary_Classification":
          last_layer_activation = "sigmoid"
          num_classes = 1
          # loss_function = 'binary_crossentropy'
          loss_function = weighted_binary_crossentropy
          # metrics = 'accuracy'
          metrics = F1Score()
      elif modeltype == "Multi_Classification":
          last_layer_activation = "softmax"
          # loss_function = 'categorical_crossentropy'
          loss_function = weighted_categorical_crossentropy
          ### try f1_score or accuracy ###########
          # metrics = 'accuracy'
          metrics = F1Score()
      else:
          last_layer_activation = "linear"
          num_classes = 1
          loss_function = "mean_squared_error"
          loss_function = weighted_mse
          metrics = "mse"
  
      if verbose:
          print(
              modeltype,
              num_classes,
              last_layer_activation,
              loss_function,
              metrics,
          )
  
      ##### This is the plain old DNN with dense and hidden layers
      # Dynamically set the layer sizes based on the input dimension
      if input_dim <= 5:
          base_size = min(
              512, input_dim * 10
          )  # Increased the potential size for more complexity
      else:
          base_size = min(
              512, input_dim * 5
          )  # Increased the potential size for more complexity
      layer_sizes = []
      for num in range(num_hidden_layers):
          layer_sizes.append(int(base_size * (1 / (num + 1))))
  
      if verbose:
          print(f"Number of hidden layers: {num_hidden_layers}")
          print(f"Neurons in each layer: {layer_sizes}")
  
      model = Sequential()
      # Input layer
      model.add(
          Dense(
              layer_sizes[0],
              input_dim=input_dim,
              activation=activation,
              kernel_regularizer=regularization,
          )
      )
      model.add(BatchNormalization())
      model.add(Dropout(dropout_rate))
  
      # Dynamic hidden layers
      for size in layer_sizes[1:]:
          model.add(
              Dense(
                  size,
                  activation=activation,
                  kernel_regularizer=regularization,
              )
          )
          model.add(BatchNormalization())
          model.add(Dropout(dropout_rate))
  
      # Output layer
      model.add(Dense(num_classes, activation=last_layer_activation))
  
      # Compile the model with the optimizer
      if modeltype == "Regression":
          model.compile(
              optimizer=Adam(learning_rate=learning_rate),
              loss=loss_function,
              metrics=[metrics],
          )  # Use MSE for regression
      elif modeltype == "Binary_Classification":
          model.compile(
              optimizer=Adam(learning_rate=learning_rate),
              loss=loss_function,
              metrics=[metrics],
          )
          # Use binary crossentropy and F1 score for binary classification
      else:
          model.compile(
              optimizer=Adam(learning_rate=learning_rate),
              loss=loss_function,
              metrics=[metrics],
          )
          # Use categorical crossentropy for multi-class
  
      return model
  
imports: import copy
  import numpy
  import pandas
  import matplotlib.pyplot
  from sklearn.metrics import mean_squared_error
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau
  from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
  from tensorflow.keras.utils import to_categorical
  import pdb
  from sklearn.metrics import accuracy_score, balanced_accuracy_score, average_precision_score, f1_score, mean_squared_error
  from sklearn.utils import shuffle
  import numpy
  from sklearn.utils import shuffle
  import copy
  from tensorflow.keras.callbacks import ReduceLROnPlateau
  import matplotlib.pyplot
  import tensorflow
  from collections import OrderedDict
  from collections import Counter
  from sklearn.utils.class_weight import compute_class_weight
  import copy
  from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback
  from sklearn.base import BaseEstimator, TransformerMixin
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Activation
  from tensorflow.keras.models import Sequential, Model
  from tensorflow.keras.regularizers import l1_l2
  from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler
  from sklearn.metrics import balanced_accuracy_score, classification_report
  import numpy
  import tensorflow
  from tensorflow.keras.layers import Input, Dense, BatchNormalization, Activation, Dropout
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.regularizers import l1_l2
  from tensorflow.keras.metrics import Metric
  import time
label_desc: Use image node_id 4 for calling a global function with example usage: _build_nn_model(input_dim, num_samples, num_classes, modeltype, batch_size, learning_rate, activation, num_hidden_layers, verbose) and returns: model