human
Solve the following python programming problem given the following code:
# Note: This layer will have a different depth of stacking
# for different stages on the model.
class StackedShiftBlocks(layers.Layer):
    """The layer containing stacked ShiftViTBlocks.

    Args:
        epsilon (float): The epsilon constant.
        mlp_dropout_rate (float): The dropout rate used in the MLP block.
        num_shift_blocks (int): The number of shift vit blocks for this stage.
        stochastic_depth_rate (float): The maximum drop path rate chosen.
        is_merge (boolean): A flag that determines the use of the Patch Merge
            layer after the shift vit blocks.
        num_div (int): The division of channels of the feature map. Defaults to 12.
        shift_pixel (int): The number of pixels to shift. Defaults to 1.
        mlp_expand_ratio (int): The ratio with which the initial dense layer of
            the MLP is expanded Defaults to 2.
    """

    def __init__(
        self,
        epsilon,
        mlp_dropout_rate,
        num_shift_blocks,
        stochastic_depth_rate,
        is_merge,
        num_div=12,
        shift_pixel=1,
        mlp_expand_ratio=2,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.epsilon = epsilon
        self.mlp_dropout_rate = mlp_dropout_rate
        self.num_shift_blocks = num_shift_blocks
        self.stochastic_depth_rate = stochastic_depth_rate
        self.is_merge = is_merge
        self.num_div = num_div
        self.shift_pixel = shift_pixel
        self.mlp_expand_ratio = mlp_expand_ratio

    def build(self, input_shapes):
        # Calculate stochastic depth probabilities.
        # Reference: https://keras.io/examples/vision/cct/#the-final-cct-model
        dpr = [
            x
            for x in np.linspace(
                start=0, stop=self.stochastic_depth_rate, num=self.num_shift_blocks
            )
        ]

        # Build the shift blocks as a list of ShiftViT Blocks
        self.shift_blocks = list()
        for num in range(self.num_shift_blocks):
            self.shift_blocks.append(
         ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to calculate stochastic depth probabilities.

Stacked Shift Blocks
Each stage will have a variable number of stacked ShiftViT Blocks, as suggested in
the paper. This is a generic layer that will contain the stacked shift vit blocks
with the patch merging layer as well. Combining the two operations (shift ViT
block and patch merging) is a design choice we picked for better code reusability.