code: def assemble_block(
      n_rows: Int,
      n_cols: Int,
      pdf: pd.DataFrame,
      cov_matrix: NDArray[(Any, Any), Float],
      row_mask: NDArray[Any],
  ) -> NDArray[Float]:
      """
      Creates a dense n_rows by n_cols matrix from the array of either sparse or dense vectors in the Pandas DataFrame
      corresponding to a group.  This matrix represents a block.
  
      Args:
           n_rows : The number of rows in the resulting matrix
           n_cols : The number of columns in the resulting matrix
           pdf : Pandas DataFrame corresponding to a group
           cov_matrix: 2D numpy array representing covariate columns that should be prepended to matrix X from the block.  Can be
              empty if covariates are not being applied.
          row_mask:  1D numpy array of size n_rows containing booleans used to mask rows from the block X before
              return.
  
      Returns:
          Dense n_rows - n_masked by n_columns matrix where the columns have been 0-centered and standard scaled.
      """
      mu = pdf["mu"].to_numpy()
      sig = pdf["sig"].to_numpy()
  
      if 0 in sig:
          raise ValueError(f"Standard deviation cannot be 0.")
  
      if row_mask.size == 0:
          row_mask = np.full(n_rows, True)
  
      if "indices" not in pdf.columns:
          X_raw = np.column_stack(pdf["values"].array)
      else:
          X_raw = np.zeros([n_rows, n_cols])
          for column, row in enumerate(
              pdf[["indices", "values"]].itertuples()
          ):
              X_raw[row.indices, column] = row.values
  
      X = (X_raw - mu) / sig
  
      if cov_matrix.any():
          return np.column_stack((cov_matrix, X))[row_mask, :]
      else:
          return X[row_mask, :]
  
imports: import itertools
  import math
  import re
  import warnings
  from typing import Any, Dict, Iterable, List, Tuple
  import numpy
  import pandas
  import pyspark.sql.functions
  import scipy.optimize
  from nptyping import Float, Int, NDArray
  from pyspark.sql import DataFrame, Row
  from pyspark.sql.window import Window
  from typeguard import typechecked
label_desc: Use image node_id 4 for calling a global function with example usage: assemble_block(n_rows, n_cols, pdf, cov_matrix, row_mask) and returns: np, X