code: def drop_path(
      input: torch.Tensor,
      drop_prob: float = 0.0,
      training: bool = False,
  ) -> torch.Tensor:
      """
      Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
  
      Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
      however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
      See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
      layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
      argument.
      """
      if drop_prob == 0.0 or not training:
          return input
      keep_prob = 1 - drop_prob
      shape = (input.shape[0],) + (1,) * (
          input.ndim - 1
      )  # work with diff dim tensors, not just 2D ConvNets
      random_tensor = keep_prob + torch.rand(
          shape, dtype=input.dtype, device=input.device
      )
      random_tensor.floor_()  # binarize
      output = input.div(keep_prob) * random_tensor
      return output
  
imports: import collections
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_timesformer import TimesformerConfig
label_desc: Use image node_id 1 for calling a global function with example usage: drop_path(input, drop_prob, training) and returns: output, input