class_docstr: Most tests done in the base class LinearOperatorDerivedClassTest.
code: def operator_and_matrix(
      self,
      build_info,
      dtype,
      use_placeholder,
      ensure_self_adjoint_and_pd=False,
  ):
      shape = list(build_info.shape)
      row = np.random.uniform(low=1.0, high=5.0, size=shape[:-1])
      col = np.random.uniform(low=1.0, high=5.0, size=shape[:-1])
  
      # Make sure first entry is the same
      row[..., 0] = col[..., 0]
  
      if ensure_self_adjoint_and_pd:
          # Note that a Toeplitz matrix generated from a linearly decreasing
          # non-negative sequence is positive definite. See
          # https://www.math.cinvestav.mx/~grudsky/Papers/118_29062012_Albrecht.pdf
          # for details.
          row = np.linspace(start=10.0, stop=1.0, num=shape[-1])
  
          # The entries for the first row and column should be the same to guarantee
          # symmetric.
          row = col
  
      lin_op_row = math_ops.cast(row, dtype=dtype)
      lin_op_col = math_ops.cast(col, dtype=dtype)
  
      if use_placeholder:
          lin_op_row = array_ops.placeholder_with_default(
              lin_op_row, shape=None
          )
          lin_op_col = array_ops.placeholder_with_default(
              lin_op_col, shape=None
          )
  
      operator = linear_operator_toeplitz.LinearOperatorToeplitz(
          row=lin_op_row,
          col=lin_op_col,
          is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
          is_positive_definite=True
          if ensure_self_adjoint_and_pd
          else None,
      )
  
      flattened_row = np.reshape(row, (-1, shape[-1]))
      flattened_col = np.reshape(col, (-1, shape[-1]))
      flattened_toeplitz = np.zeros(
          [flattened_row.shape[0], shape[-1], shape[-1]]
      )
      for i in range(flattened_row.shape[0]):
          flattened_toeplitz[i] = scipy.linalg.toeplitz(
              flattened_col[i], flattened_row[i]
          )
      matrix = np.reshape(flattened_toeplitz, shape)
      matrix = math_ops.cast(matrix, dtype=dtype)
  
      return operator, matrix
  
imports: import contextlib
  import numpy
  import scipy.linalg
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.linalg import linalg
  from tensorflow.python.ops.linalg import linear_operator_test_util
  from tensorflow.python.ops.linalg import linear_operator_toeplitz
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the LinearOperatorToeplitzTest obj's underlying member method code with example usage: obj.operator_and_matrix(build_info, dtype, use_placeholder, ensure__adjoint_and_pd) and returns: operator,  matrix