code: def __init__(self, arg1, shape=None, dtype=None, copy=False):
      _data_matrix.__init__(self)
  
      if issparse(arg1):
          if arg1.format == "dia":
              if copy:
                  arg1 = arg1.copy()
              self.data = arg1.data
              self.offsets = arg1.offsets
              self._shape = check_shape(arg1.shape)
          else:
              if arg1.format == self.format and copy:
                  A = arg1.copy()
              else:
                  A = arg1.todia()
              self.data = A.data
              self.offsets = A.offsets
              self._shape = check_shape(A.shape)
      elif isinstance(arg1, tuple):
          if isshape(arg1):
              # It's a tuple of matrix dimensions (M, N)
              # create empty matrix
              self._shape = check_shape(arg1)
              self.data = np.zeros(
                  (0, 0), getdtype(dtype, default=float)
              )
              idx_dtype = self._get_index_dtype(maxval=max(self.shape))
              self.offsets = np.zeros((0), dtype=idx_dtype)
          else:
              try:
                  # Try interpreting it as (data, offsets)
                  data, offsets = arg1
              except Exception as e:
                  message = (
                      "unrecognized form for dia_array constructor"
                  )
                  raise ValueError(message) from e
              else:
                  if shape is None:
                      raise ValueError("expected a shape argument")
                  self.data = np.atleast_2d(
                      np.array(arg1[0], dtype=dtype, copy=copy)
                  )
                  offsets = np.array(
                      arg1[1],
                      dtype=self._get_index_dtype(maxval=max(shape)),
                      copy=copy,
                  )
                  self.offsets = np.atleast_1d(offsets)
                  self._shape = check_shape(shape)
      else:
          # must be dense, convert to COO first, then to DIA
          try:
              arg1 = np.asarray(arg1)
          except Exception as e:
              raise ValueError(
                  "unrecognized form for"
                  " %s_matrix constructor" % self.format
              ) from e
          A = self._coo_container(
              arg1, dtype=dtype, shape=shape
          ).todia()
          self.data = A.data
          self.offsets = A.offsets
          self._shape = check_shape(A.shape)
  
      if dtype is not None:
          self.data = self.data.astype(dtype)
  
      # check format
      if self.offsets.ndim != 1:
          raise ValueError("offsets array must have rank 1")
  
      if self.data.ndim != 2:
          raise ValueError("data array must have rank 2")
  
      if self.data.shape[0] != len(self.offsets):
          raise ValueError(
              "number of diagonals (%d) "
              "does not match the number of offsets (%d)"
              % (self.data.shape[0], len(self.offsets))
          )
  
      if len(np.unique(self.offsets)) != len(self.offsets):
          raise ValueError("offset array contains duplicate values")
  
imports: import numpy
  from ._matrix import spmatrix
  from ._base import issparse, _formats, _spbase, sparray
  from ._data import _data_matrix
  from ._sputils import isshape, upcast_char, getdtype, get_sum_dtype, validateaxis, check_shape
  from ._sparsetools import dia_matvec
label_desc: Use image node_id 1 to create a new _dia_base object from inherited base classes: _data_matrix with example: obj = _dia_base(arg1, shape, dtype, copy)