class_docstr: Layer Integrated Gradients is a variant of Integrated Gradients that assigns
  an importance score to layer inputs or outputs, depending on whether we
  attribute to the former or to the latter one.
  
  Integrated Gradients is an axiomatic model interpretability algorithm that
  attributes / assigns an importance score to each input feature by approximating
  the integral of gradients of the model's output with respect to the inputs
  along the path (straight line) from given baselines / references to inputs.
  
  Baselines can be provided as input arguments to attribute method.
  To approximate the integral we can choose to use either a variant of
  Riemann sum or Gauss-Legendre quadrature rule.
  
  More details regarding the integrated gradients method can be found in the
  original paper:
  https://arxiv.org/abs/1703.01365
code: def has_convergence_delta(self) -> bool:
      return True
  
imports: import functools
  import warnings
  from typing import Any, Callable, List, overload, Tuple, Union
  import torch
  from captum._utils.common import _extract_device, _format_additional_forward_args, _format_outputs
  from captum._utils.gradient import _forward_layer_eval, _run_forward
  from captum._utils.typing import BaselineType, Literal, ModuleOrModuleList, TargetType
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.attr._utils.common import _format_input_baseline, _tensorize_baseline, _validate_input
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn.parallel.scatter_gather import scatter
label_desc: Use image node_id 6 for calling the LayerIntegratedGradients obj's underlying member method code with example usage: obj.has_convergence_delta() and returns: True