You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: block.m
    path of file: ./repos/transformer-models/+gpt2/+layer
    
    the code of the file until where you have to start completion: function [X, present] = block(X, past, weights, hyperParameters)
% block   Transformer block for GPT-2
%
%   [X, present] = block(X, past, weights, hyperParameters) computes a
%   GPT-2 style transformer block on the input X as described in [1] (see
%   Section 2.3). One difference between this style of transformer block
%   and others is that this block uses layer normalization at the
%   beginning.
%
%   Inputs:
%       X               - A (numFeatures*numHeads)-by-numInputSubwords
%                         input array.
%       past            - A numFeatures-by-numPastSubwords-by-numHeads-by-2
%                         array. This contains the 'keys' and 'values' for
%                         past subwords. These are needed to predict future
%                         outputs in an autoregressive manner. 'keys' are
%                         stored in past(:,:,:,1) and 'values' are stored
%                         in past(:,:,:,2).
%       weights         - The weights for the transformer block stored in a
%                         struct. In this block we have:
%                           - ln_1_g_0: Weight vector for the first layer
%                             normalization.
%                           - ln_1_b_0: Bias vector for the first layer
%                             normalization.
%                           - ln_2_g_0: Weight vector for the second layer
%                             normalization.
%                           - ln_2_b_0: Bias vector for the second layer
%                             normalization.
%                         In the attention sub-block:
%                           - attn_c_attn_w_0: A weight matrix for the
%                             first fully connected layer.
%                           - attn_c_attn_b_0: A bias vector for the first
%                             fully connected layer.
%                           - attn_c_proj_w_0: A weight matrix for the
%                             final fully connected layer.
%                           - attn_c_proj_b_0: A bias vector for the final
%                             fully connected layer.
%                         In the multi-layer perceptron block:
%                           - mlp_c_fc_w_0: A weight matrix for the first
%                             fully connected layer.
%                           - mlp_c_fc_b_0: A bias vector for the first
%                             fully connected layer.
%                           - mlp_c_proj_w_0: A weight matrix for the
%                             second fully connected layer.
%                           - mlp_c_proj_b_0: A bias vector for the second
%                             fully connected layer.
%       numHeads        - The number of attention heads. This is a
%                         hyper-parameter.
%
%   Outputs:
%       Z               - A (numFeatures*numHeads)-by-numInputSubwords
%                         output array.
%       present         - A numFeatures-by-numAllSubwords-by-numHeads-by-2
%                         array. This contains the 'keys' and 'values' that
%                         are created from inputs. These need to passed
%                         back in as the 'past' input if we want to predict
%                         future outputs in an autoregressive manner. 'keys'
%                         are stored in present(:,:,:,1) and 'values' are
%                         stored in present(:,:,:,2).
%
%   References:
%
%   [1] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei,

    
function [X, present] = block(X, past, weights, hyperParameters)
% block   Transformer block for GPT-2
%
%   [X, present] = block(X, past, weights, hyperParameters) computes a
%   GPT-2 style transformer block on the input X as described in [1] (see
%   Section 2.3). One difference between this style of transformer block
%   and others is that this block uses layer normalization at the
%   beginning.
%
%   Inputs:
%       X               - A (numFeatures*numHeads)-by-numInputSubwords
%                         input array.
%       past            - A numFeatures-by-numPastSubwords-by-numHeads-by-2
%                         array. This contains the 'keys' and 'values' for
%                         past subwords. These are needed to predict future
%                         outputs in an autoregressive manner. 'keys' are
%                         stored in past(:,:,:,1) and 'values' are stored
%                         in past(:,:,:,2).
%       weights         - The weights for the transformer block stored in a
%                         struct. In this block we have:
%                           - ln_1_g_0: Weight vector for the first layer
%                             normalization.
%                           - ln_1_b_0: Bias vector for the first layer
%                             normalization.
%                           - ln_2_g_0: Weight vector for the second layer
%                             normalization.
%                           - ln_2_b_0: Bias vector for the second layer
%                             normalization.
%                         In the attention sub-block:
%                           - attn_c_attn_w_0: A weight matrix for the
%                             first fully connected layer.
%                           - attn_c_attn_b_0: A bias vector for the first
%                             fully connected layer.
%                           - attn_c_proj_w_0: A weight matrix for the
%                             final fully connected layer.
%                           - attn_c_proj_b_0: A bias vector for the final
%                             fully connected layer.
%                         In the multi-layer perceptron block:
%                           - mlp_c_fc_w_0: A weight matrix for the first
%                             fully connected layer.
%                           - mlp_c_fc_b_0: A bias vector for the first
%                             fully connected layer.
%                           - mlp_c_proj_w_0: A weight matrix for the
%                             second fully connected layer.
%                           - mlp_c_proj_b_0: A bias vector for the second
%                             fully connected layer.
%       numHeads        - The number of attention heads. This is a
%                         hyper-parameter.
%
%   Outputs:
%       Z               - A (numFeatures*numHeads)-by-numInputSubwords
%                         output array.
%       present         - A numFeatures-by-numAllSubwords-by-numHeads-by-2
%                         array. This contains the 'keys' and 'values' that
%                         are created from inputs. These need to passed
%                         back in as the 'past' input if we want to predict
%                         future outputs in an autoregressive manner. 'keys'
%                         are stored in present(:,:,:,1) and 'values' are
%                         stored in present(:,:,:,2).
%
%   References:
%
%   [1] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei,
%       Ilya Sutskever, "Language Models are Unsupervised Multitask
%       Learners",
%       https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

XNorm1 = transformer.layer.normalization(X, ...
    weights.ln_1_g_0, weights.ln_1_b_0);

[A, present] = transformer.layer.attention(XNorm1, past, weights, hyperParameters);

X = X + A;
 
XNorm2 = transformer.layer.normalization(X, ...
    weights.ln_2_g_0, weights.ln_2_b_0);

M = transformer.layer.multiLayerPerceptron(XNorm2, weights);

X = X + M;

end