code: def to_matrix_with_info(x, dtype, copy=False):
      """
      Given some array-like X (which should be either a numpy ndarray or a pandas
      DataFrame), convert it into a numpy matrix of the given dtype.
      """
      # Make sure it's array-like at all.
      if (
          not hasattr(x, "__len__")
          and not hasattr(x, "shape")
          and not hasattr(x, "__array__")
      ):
          raise TypeError("given argument is not array-like")
  
      if isinstance(x, np.ndarray):
          # It is already an ndarray, so the vector of info is all 0s (all numeric).
          if len(x.shape) < 2:
              d = np.zeros(1, dtype=bool)
          else:
              d = np.zeros([x.shape[1]], dtype=bool)
  
          # Copy the matrix if needed.
          if copy:
              return (x.copy(order="C"), True, d)
          else:
              return (x, False, d)
  
      if isinstance(x, pd.DataFrame) or isinstance(x, pd.Series):
          # It's a pandas dataframe.  So we need to see if any of the dtypes are
          # categorical or object, and if so, we need to convert them.  First see if
          # we can take a shortcut without copying.
          dtype_array = (
              x.dtypes.values if len(x.dtypes) > 0 else [x.dtypes]
          )
          if (
              not any(
                  isinstance(t, CategoricalDtype) for t in dtype_array
              )
              and not np.dtype(object) in dtype_array
              and not np.dtype(str) in dtype_array
              and not np.dtype(unicode) in dtype_array
          ):
              # We can just return the matrix as-is; it's all numeric.
              t = to_matrix(x, dtype=dtype, copy=copy)
              if len(x.shape) < 2:
                  d = np.zeros(1, dtype=bool)
              else:
                  d = np.zeros([x.shape[1]], dtype=bool)
              return (t[0], t[1], d)
  
          if (
              np.dtype(str) in dtype_array
              or np.dtype(unicode) in dtype_array
          ):
              raise TypeError(
                  "cannot convert matrices with string types"
              )
  
          if np.dtype(buffer) in dtype_array:
              raise TypeError("'buffer' dtype not supported")
  
          # If we get to here, then we are going to need to do some type conversion,
          # so go ahead and copy the dataframe and we'll work with y to make
          # modifications.
          y = x
          d = np.zeros([x.shape[1]], dtype=bool)
  
          # Convert any 'object', 'str', or 'unicode' types to categorical.
          convertColumns = x.select_dtypes(["object"])
          if not convertColumns.empty:
              y[convertColumns] = y[convertColumns].astype("category")
  
          catColumns = x.select_dtypes(["category"]).columns
          if len(catColumns) > 0:
              # Do actual conversion to numeric types.  This converts to an int type.
              y = x  # Copy it... not great...
  
              # Note that this will map NaNs (missing values or unknown categories) to
              # -1, so we will have to convert those back to NaN.
              y[catColumns] = (
                  y[catColumns]
                  .apply(lambda c: c.cat.codes)
                  .astype("double")
              )
              y[catColumns].replace(to_replace=[-1], value=float("NaN"))
  
              # Construct dataset information: 1s represent categorical data, 0s
              # represent otherwise.
              catColumnIndices = [
                  y.columns.get_loc(i) for i in catColumns
              ]
              d[catColumnIndices] = 1
  
          # We'll have to force the second part of the tuple (whether or not to take
          # ownership) to true.
          t = to_matrix(y.apply(pd.to_numeric), dtype=dtype)
          return (t[0], True, d)
  
      if isinstance(x, list):
          # Get the number of dimensions.
          dims = 0
          if isinstance(x[0], list):
              dims = len(x[0])
          else:
              dims = len(x)
  
          d = np.zeros([dims])
          out = np.array(
              x, dtype=dtype, copy=copy
          )  # Try to avoid copy...
  
          # Since we don't have a great way to check if these are using the same
          # memory location, we will probe manually (ugh).
          oldval = x[0][0]
          x[0][0] *= 2
          alias = False
          if out.flat[0] == x[0][0]:
              alias = True
          x[0][0] = oldval
  
          return (out, not alias, d)
  
      # If we got here, the type is not known.
      raise TypeError(
          "given matrix is not a numpy ndarray or pandas DataFrame or "
          "Python array; not supported at this time"
      )
  
imports: import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: to_matrix_with_info(x, dtype, copy) and returns: t,  True,  d, out,  unknown,  d, x,  True,  d, x,  False,  d, t,  t,  d