Module/Function Name: VitTransformerBlock

This is a transformer block used in the Vision Transformer (ViT) denoiser model. The block takes the input dimension, number of attention heads, dimension of each attention head, dimension of the feed-forward network, expansion factor for the feed-forward network, and dropout rate as parameters. It then normalizes the input, computes self-attention, and then passes it through a feed-forward network.

Parameters:
| Parameter         | Description |
| ----------------- | ----------- |
| dim               | The input dimension of the block. |
| heads             | The number of attention heads. |
| dim_head          | The dimension of each attention head. |
| mlp_dim           | The dimension of the feed-forward network. |
| expansion         | The expansion factor for the feed-forward network. |
| dropout           | The dropout rate. |

Example

# Usage example 1:
import torch
import torch.nn as nn

input_dim = 256
num_heads = 3
dim_head = 64
feedforward_dim = 512
expansion_factor = 3
dropout_rate = 0.1

transformer_block = VitTransformerBlock(
    input_dim, num_heads, dim_head, feedforward_dim, expansion_factor, dropout_rate
)
input_tensor = torch.randn(
    1, 3, 256, 512
)  # Batch size of 5, sequence length of 256, input dimension of 256
output = transformer_block(input_tensor)

# Usage example 2:
input_dim = 256
num_heads = 4
dim_head = 64
feedforward_dim = 512
expansion_factor = 3
dropout_rate = 0.1
transformer_block = VitTransformerBlock(
    input_dim, num_heads, dim_head, feedforward_dim, expansion_factor, dropout_rate
)
input_tensor = torch.randn(
    1, 4, 64, 256
)  # Batch size of 4, sequence length of 64 input dimension of 256
output = transformer_block(input_tensor)

The VitTransformerBlock class represents a self-contained instance of a transformer block module used in the Vision Transformer architecture. The block has been designed and implemented to perform various operations such as self-attention and feed-forward network processing efficiently and effectively. It takes into account all the relevant design considerations and parameters required for its successful operation.

It consists of a number of attributes representing its state and components, including the input dimension, number of attention heads, dimensions of each attention head, feed-forward network structure, expansion factor, and dropout rate. These attributes encapsulate essential details about the block and provide information about its intended functionality and behavior.

The class features an initializer method to set up the essential components and state of the block. During the initialization process, the relevant parameters are used to configure the instance to operate effectively in accordance with the specified dimensions and requirements. The block also defines a forward method to perform the forward pass and processing of input data through the self-attention mechanism and the feed-forward network.

Overall, the VitTransformerBlock class encapsulates the core functionality and operation of a transformer block module used in the Vision Transformer architecture, covering all aspects of its design, implementation, and functional behavior in the context of the ViT denoiser model.