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 list of numbers is confusing because it is not obvious which numbers are x and which are y coordinates. It will also be difficult to maintain (add or delete coordinates to the lists). It would be better to create a matrix (a list of list). Each item in the list will be a list of x and y coordinates.Abstract Data Types (ADT) is a custom data type created by the programmer to provide meaning to the data in your program. It is not built into the programming language. ADT are created with custom constructor and selector functions (blocks). ADT are a form of abstraction because they make are program easier to understand, read, and debug.Step 1: Create A Reporter block (the constructor) that will take two numbers as the domain (x and y coordinates) and output a list as the range.Step 2: Create two report blocks (the selectors) that will take the list created by the contructor block as the domain and output a single number as the range (one the x coordinate the second block the y coordinate).Step 3: Create a list that stores the necessary Coordinte Points to draw your specified shape Step 4: Create a Draw Shape Command Block whose domain will be the list of Coordinate Points create in step 3. The command block will draw the shape on the screen by using a the for each item block and the selector blocks created in Step 2.Lists and Numbers are examples of Primitive Data Types240,00,100-240,00,-100240,0