input: Here is the function definition and test case based on the provided description:
  
  
  def train_cat_dog_classifier(dataset_path: str) -> keras.models.Sequential:
      """
      This function collates a machine learning model using Keras in Python. The model is designed to distinguish between images of cats and dogs.
      The function takes a directory path for the dataset as input and returns a trained Keras model.
      The dataset directory should contain subdirectories 'cats' and 'dogs' with their respective images.
  
      Example:
      >>> model = train_cat_dog_classifier('/path/to/dataset')
      >>> model.evaluate(test_generator)
      [0.2830, 0.9235]
      """
      import numpy as np
      import keras
      from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
      from keras.models import Sequential
      from keras.preprocessing.image import ImageDataGenerator
      
      # Create the neural network model
      model = Sequential()
      
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
      
      # Hidden layers
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      
      # Output layer
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
      
      # Compile the model
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      
      # Create data generator
      datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      
      # Fit the model
      train_generator = datagen.flow_from_directory(directory=dataset_path, target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
      
      return model
  
output:     
      # Create data generator for validation
      test_datagen = ImageDataGenerator(rescale=1./255)
      test_generator = test_datagen.flow_from_directory(directory=dataset_path, target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      
      # Fit the model with validation data
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
      
      return model
  