Answer:
In a neural network, the flatten layer is a type of layer that flattens the input tensor into a two-dimensional matrix. This is often used before passing the data to a dense layer, which performs the actual learning in the network.
The components of a flatten layer are the elements of the input tensor that are flattened into the two-dimensional matrix. Each component of the tensor corresponds to a single element of the matrix, and the flatten layer simply rearranges these elements into a new matrix with the same data.
In summary, the function of the components in a flatten layer is to represent the elements of the input tensor, which are rearranged into a two-dimensional matrix by the flatten layer. This matrix can then be passed to a dense layer for further processing.