class_docstr: Compares the results of a slice assignment using Tensorflow and numpy.
class_docstr_tok: ["Compares","the","results","of","a","slice","assignment","using","Tensorflow","and","numpy","."]
code: def __init__(self, test, x, dtype):
      self.dtype = dtype
      self.test = test
      self.x_np = np.array(x).astype(dtype)
      # Randomly start on mode 0 or 1.
      self.which_mode = np.random.randint(2, size=1)[0]
  
imports: import re
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_state_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import googletest
  from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
label_desc: Use image node_id 1 to create a new StridedSliceAssignChecker object from inherited base classes: object with example: obj = StridedSliceAssignChecker(test, x, dtype)