code: def fit(self, inputs):
      """
      Fits the K-means algorithm to the input data.
  
      Args:
          inputs (torch.Tensor): Input data of shape (batch_size, n_features).
  
      Returns:
          tuple: A tuple containing the labels of the input data with respect to the identified clusters
          and the cluster centers themselves. The labels have a shape of (batch_size,) and the
          cluster centers have a shape of (n_clusters, n_features).
  
      Raises:
          ValueError: If the number of clusters is less than or equal to 0.
      """
      batch_size, _ = inputs.shape
  
      # Initialize centroids randomly from the data points
      centroid_indices = torch.randint(
          0, batch_size, (self.n_clusters,)
      )
      self.cluster_centers_ = inputs[centroid_indices]
  
      # Run the k-means algorithm for max_iter iterations
      for _ in range(self.max_iter):
          # Compute the distance between each data point and each centroid
          distances = torch.cdist(inputs, self.cluster_centers_)
  
          # Assign each data point to the closest centroid
          self.labels_ = torch.argmin(distances, dim=1)
  
          # Update the centroids to be the mean of the data points assigned to them
          for j in range(self.n_clusters):
              mask = self.labels_ == j
              if mask.any():
                  self.cluster_centers_[j] = inputs[mask].mean(dim=0)
      # this line returns labels and centoids of the results
      return self.labels_, self.cluster_centers_
  
imports: import torch
label_desc: Use image node_id 2 for calling the KMeans obj's underlying member method code with example usage: obj.fit(inputs) and returns: self,  self