code: def crnn_model(
      width=100,
      n_vars=6,
      n_classes=7,
      conv_kernel_size=5,
      conv_filters=3,
      lstm_units=3,
  ):
      input_shape = (width, n_vars)
      model = Sequential()
      model.add(
          Conv1D(
              filters=conv_filters,
              kernel_size=conv_kernel_size,
              padding="valid",
              activation="relu",
              input_shape=input_shape,
          )
      )
      model.add(
          Conv1D(
              filters=conv_filters,
              kernel_size=conv_kernel_size,
              padding="valid",
              activation="relu",
          )
      )
      model.add(
          LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1)
      )
      model.add(Dense(n_classes, activation="softmax"))
  
      model.compile(
          loss="categorical_crossentropy",
          optimizer="adam",
          metrics=["accuracy"],
      )
  
      return model
  
file_path: seglearn/examples/plot_segment_rep.py
imports: import matplotlib.image
  import matplotlib.pyplot
  from tensorflow.python.keras.layers import Dense, LSTM, Conv1D
  from tensorflow.python.keras.models import Sequential
  from tensorflow.python.keras.wrappers.scikit_learn import KerasClassifier
  from sklearn.model_selection import train_test_split
  from seglearn.datasets import load_watch
  from seglearn.pipe import Pype
  from seglearn.transform import Segment
label_desc: Use image node_id 1 for calling a global function with example usage: crnn_model(width, n_vars, n_classes, conv_kernel_size, conv_filters, lstm_units) and returns: model