def call(self, inputs):
    return tf.image.rgb_to_grayscale(inputs)

def call(self, inputs):
    # Ensure input has a channel dimension
    if inputs.shape.rank == 3:  # Shape: (None, 224, 224)
        inputs = tf.expand_dims(inputs, axis=-1)  # Shape: (None, 224, 224, 1)
    return tf.image.grayscale_to_rgb(inputs)

def compute_output_shape(self, input_shape):
    return input_shape[:-1] + (3,)  # Output has 3 channels

def call(self, inputs):
    if tf.executing_eagerly():
        # PIL-based enhancements (for debugging and visualization)
        img = tf.keras.preprocessing.image.array_to_img(inputs[0])  # Access first image in batch
        brightness_factor = 1 + (34 / 100)
        x_enhanced = ImageEnhance.Brightness(img).enhance(brightness_factor)
        contrast_factor = 1 + (30 / 100)
        x_enhanced = ImageEnhance.Contrast(x_enhanced).enhance(contrast_factor)
        sharpness_factor = 1 + (70 / 100)
        x_enhanced = ImageEnhance.Sharpness(x_enhanced).enhance(sharpness_factor)
        x_enhanced = x_enhanced.filter(ImageFilter.MedianFilter(size=1))

        # Convert back to tensor
        x_enhanced = tf.keras.preprocessing.image.img_to_array(x_enhanced)
        x_enhanced = tf.expand_dims(x_enhanced, 0)  # Restore batch dimension
        x_enhanced = tf.cast(x_enhanced, inputs.dtype)
    else:
        # Placeholder for TensorFlow-based enhancements during training
        x_enhanced = inputs
    return x_enhanced

def call(self, inputs):
    # Placeholder for stroke width computation logic
    return inputs

    # Check the number of channels
    num_channels = inputs.shape[-1]

    # Define Roberts Cross kernels
    kernel_x = tf.constant([[1, 0], [0, -1]], dtype=tf.float32)
    kernel_y = tf.constant([[0, 1], [-1, 0]], dtype=tf.float32)

    # Reshape kernels to match the number of input channels
    kernel_x = tf.reshape(kernel_x, [2, 2, 1, 1])
    kernel_y = tf.reshape(kernel_y, [2, 2, 1, 1])

    # Apply convolution using Roberts Cross kernels for each channel
    grad_x = [tf.nn.conv2d(inputs_float32[..., i:i+1], kernel_x, strides=[1, 1, 1, 1], padding='SAME') for i in range(num_channels)]
    grad_y = [tf.nn.conv2d(inputs_float32[..., i:i+1], kernel_y, strides=[1, 1, 1, 1], padding='SAME') for i in range(num_channels)]

    # Concatenate the gradients along the channel dimension (if more than one channel)
    grad_x = tf.concat(grad_x, axis=-1)
    grad_y = tf.concat(grad_y, axis=-1)

    # Compute the gradient magnitude
    grad_magnitude = tf.sqrt(tf.square(grad_x) + tf.square(grad_y))

    # Normalize the gradient magnitudes
    grad_magnitude /= tf.reduce_max(grad_magnitude)

    # Return the result with the same number of channels as input
    return grad_magnitude

def compute_output_shape(self, input_shape):
    return input_shape

def call(self, inputs):
    # Ensure inputs have the shape (batch_size, height, width, 1) for grayscale
    if inputs.shape[-1] != 1:
        raise ValueError("Input must be a grayscale image with a single channel.")

    # Define the kernel for dilation and erosion
    kernel = tf.ones(tuple(self.kernel_size) + (1,), dtype=tf.float32)

    # Perform dilation
    dilated_image = tf.image.extract_patches(
        images=inputs,
        sizes=[1, self.kernel_size[0], self.kernel_size[1], 1],
        strides=[1, 1, 1, 1],
        rates=[1, 1, 1, 1],
        padding='SAME'
    )
    dilated_image = tf.reduce_max(dilated_image, axis=-1, keepdims=True)

    # Perform erosion
    eroded_image = tf.image.extract_patches(
        images=dilated_image,
        sizes=[1, self.kernel_size[0], self.kernel_size[1], 1],
        strides=[1, 1, 1, 1],
        rates=[1, 1, 1, 1],
        padding='SAME'
    )
    eroded_image = tf.reduce_min(eroded_image, axis=-1, keepdims=True)
    return eroded_image

def compute_output_shape(self, input_shape):
    return input_shape  # Ensure output shape matches input shape

def call(self, inputs):
    squeezed = tf.squeeze(inputs, axis=self.axis)  # Remove extra dimensions

    # If squeezed shape has 3 dimensions and we have grayscale images, restore the channel dimension
    if len(squeezed.shape) == 3:  # (batch, height, width) for grayscale image
        restored = tf.expand_dims(squeezed, axis=-1)  # (batch_size, 224, 224, 1)
    else:
        restored = squeezed  # No expansion needed, already 4D

    return restored

# Handle RGBA images (if the image has an alpha channel)
if img_array.shape[-1] == 4:
    img_array = img_array[..., :3]

img_array = np.expand_dims(img_array, 0) / 255.0
return img_array

# Optionally print predictions for debugging
print(f"Prediction probabilities: {predictions[0]}")

class_label = class_labels[predicted_class_index]
diagnostic_prompt = diagnostic_messages[class_label]
return f"This handwriting is \"{class_label}\".\n\n{diagnostic_prompt}"