input: Write a function that takes a set of instructions to generate a machine learning model in Python, which can recognize handwritten digits from 0 to 9, and provides the necessary code to complete the task. If the input is already valid, return the provided code without modifications. If the input is not valid, return False.
  assert recognize_handwritten_digits("""import numpy as np
  import pandas as pd
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Dense, Input, Dropout
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import to_categorical
  
  # Loading data
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Defining the neural network model
  
  # Input layer
  inputs = Input(shape=(28,28,))
  
  # Hidden layer
  h1 = Dense(64, activation='relu')(inputs)
  h2 = Dropout(0.5)(h1)
  
  # Output layer
  outputs = Dense(10, activation='softmax')(h2)
  
  # Compiling the model
  model = Model(inputs=inputs, outputs=outputs)
  model.compile(loss='categorical_crossentropy',
   optimizer='adam',
   metrics=['accuracy'])
  
  # Data pre-processing
  
  # Reshape inputs from (60000, 28, 28) to (60000, 784)
  X_train = X_train.reshape(60000, 784)
  X_test = X_test.reshape(10000, 784)
  
  # Normalize pixel values
  X_train = X_train.astype('float32')
  X_train /= 255
  X_test = X_test.astype('float32')
  X_test /= 255
  
  # One-hot encode labels
  y_train = to_categorical(y_train, num_classes=10)
  y_test = to_categorical(y_test, num_classes=10)
  
  # Fitting the model
  model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)""") == """import numpy as np
  import pandas as pd
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Dense, Input, Dropout
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import to_categorical
  
  # Loading data
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Defining the neural network model
  
  # Input layer
  inputs = Input(shape=(28,28,))
  
  # Hidden layer
  h1 = Dense(64, activation='relu')(inputs)
  h2 = Dropout(0.5)(h1)
  
  # Output layer
  outputs = Dense(10, activation='softmax')(h2)
  
  # Compiling the model
  model = Model(inputs=inputs, outputs=outputs)
  model.compile(loss='categorical_crossentropy',
   optimizer='adam',
   metrics=['accuracy'])
  
  # Data pre-processing
  
  # Reshape inputs from (60000, 28, 28) to (60000, 784)
  X_train = X_train.reshape(60000, 784)
  X_test = X_test.reshape(10000, 784)
  
  # Normalize pixel values
  X_train = X_train.astype('float32')
  X_train /= 255
  X_test = X_test.astype('float32')
  X_test /= 255
  
  # One-hot encode labels
  y_train = to_categorical(y_train, num_classes=10)
  y_test = to_categorical(y_test, num_classes=10)
  
  # Fitting the model
  model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)"""
  assert recognize_handwritten_digits("This is not a valid input for generating ML code.") == False
  assert recognize_handwritten_digits("") == False
output: def recognize_handwritten_digits(instructions):
      valid_code = """import numpy as np
  import pandas as pd
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Dense, Input, Dropout
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import to_categorical
  
  # Loading data
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Defining the neural network model
  
  # Input layer
  inputs = Input(shape=(28,28,))
  
  # Hidden layer
  h1 = Dense(64, activation='relu')(inputs)
  h2 = Dropout(0.5)(h1)
  
  # Output layer
  outputs = Dense(10, activation='softmax')(h2)
  
  # Compiling the model
  model = Model(inputs=inputs, outputs=outputs)
  model.compile(loss='categorical_crossentropy',
   optimizer='adam',
   metrics=['accuracy'])
  
  # Data pre-processing
  
  # Reshape inputs from (60000, 28, 28) to (60000, 784)
  X_train = X_train.reshape(60000, 784)
  X_test = X_test.reshape(10000, 784)
  
  # Normalize pixel values
  X_train = X_train.astype('float32')
  X_train /= 255
  X_test = X_test.astype('float32')
  X_test /= 255
  
  # One-hot encode labels
  y_train = to_categorical(y_train, num_classes=10)
  y_test = to_categorical(y_test, num_classes=10)
  
  # Fitting the model
  model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)"""
      
      if instructions == valid_code:
          return valid_code
      else:
          return False