code: def _static_check_for_same_dimensions(operators):
      """ValueError if operators determined to have different dimensions."""
      if len(operators) < 2:
          return
  
      domain_dimensions = [
          (op.name, tensor_shape.dimension_value(op.domain_dimension))
          for op in operators
          if tensor_shape.dimension_value(op.domain_dimension)
          is not None
      ]
      if len(set(value for name, value in domain_dimensions)) > 1:
          raise ValueError(
              f"All `operators` must have the same `domain_dimension`. "
              f"Received: {domain_dimensions}."
          )
  
      range_dimensions = [
          (op.name, tensor_shape.dimension_value(op.range_dimension))
          for op in operators
          if tensor_shape.dimension_value(op.range_dimension)
          is not None
      ]
      if len(set(value for name, value in range_dimensions)) > 1:
          raise ValueError(
              f"All operators must have the same `range_dimension`. "
              f"Received: {range_dimensions}."
          )
  
imports: import abc
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops.linalg import linear_operator
  from tensorflow.python.ops.linalg import linear_operator_diag
  from tensorflow.python.ops.linalg import linear_operator_full_matrix
  from tensorflow.python.ops.linalg import linear_operator_identity
  from tensorflow.python.ops.linalg import linear_operator_lower_triangular
label_desc: Use image node_id 4 for calling a global function with example usage: _static_check_for_same_dimensions(operators) without return types