class_docstr: The ImplicitDataset class stores and parses a sequence of user implicit feedback for training or evaluation. It extends the 
  functionality of the Dataset class.
  
  Parameters
  ----------
  raw_data: numpy structured array
      Input raw data. Other legacy formats (e.g., sparse matrix) are supported but not recommended.
  max_user: int
      Maximum number of users in the recommendation system.
  max_item: int
      Maximum number of items in the recommendation system.
  name: str
      Name of the dataset. 
  
  Notes  
  -----
  The ImplicitDataset class parses the input :code:`raw_data` into structured dictionaries (consumed by samplers or model 
  trainer). This class expects :code:`raw_data` as a numpy structured array, where each row represents a data 
  point and contains *at least* two keys:
  
  * :code:`user_id`: the user involved in the interaction.
  * :code:`item_id`: the item involved in the interaction.
  
  :code:`raw_data` might contain other keys, such as :code:`timestamp`, and :code:`location`, etc.
  based on the use cases of different recommendation systems. An user should be uniquely and numerically indexed 
  from 0 to :code:`total_number_of_users - 1`. The items should be indexed likewise.
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docstr: Number of unique items.
  
  Returns
  -------
  int
      Number of unique items.
docstr_tok: ["Number","of","unique","items",".","Returns","--","--","--","-","int","Number","of","unique","items","."]
code: def unique_item_count(self):
      """Number of unique items.
  
      Returns
      -------
      int
          Number of unique items.
      """
      return self._num_item
  
file_path: openrec/openrec/tf1/legacy/utils/implicit_dataset.py
imports: import numpy
  from openrec.tf1.legacy.utils import Dataset
label_desc: Use image node_id 9 for calling the ImplicitDataset obj's underlying member method code with example usage: obj.unique_item_count() and returns: self