code: def gather_features(
      image_features,
      text_features,
      local_loss=False,
      gather_with_grad=False,
  ):
      """
      Gathers image and text features across multiple data parallel processes.
  
      This function is designed to work in a distributed environment where multiple
      processes are handling different portions of data. It gathers the image and text
      features from all processes to form a complete set of features across the entire dataset.
      This is crucial for calculating loss in models like CLIP, especially when the model is
      trained in a data parallel fashion.
  
      Parameters:
      image_features (Tensor): A tensor containing the image features.
      text_features (Tensor): A tensor containing the text features.
      local_loss (bool, optional): A flag to determine whether to use local loss calculation.
                                   Defaults to False.
      gather_with_grad (bool, optional): A flag to enable gathering with gradient computation.
                                         This is not currently working in the latest PyTorch version.
                                         Defaults to False.
  
      Returns:
      Tuple[Tensor, Tensor]: A tuple containing the gathered image features and text features
                             across all processes.
      """
      data_parallel_world_size = (
          parallel_state.get_data_parallel_world_size()
      )
      data_parallel_rank = parallel_state.get_data_parallel_rank()
      data_parallel_group = parallel_state.get_data_parallel_group()
  
      if gather_with_grad:
          # TODO (yuya): this is not working in current version of pytorch
          # https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py#L48
          all_image_features = torch.cat(
              torch.distributed.nn.all_gather(image_features), dim=0
          )
          all_text_features = torch.cat(
              torch.distributed.nn.all_gather(text_features), dim=0
          )
  
      else:
          gathered_image_features = [
              torch.zeros_like(image_features)
              for _ in range(data_parallel_world_size)
          ]
          gathered_text_features = [
              torch.zeros_like(text_features)
              for _ in range(data_parallel_world_size)
          ]
          dist.all_gather(
              gathered_image_features,
              image_features,
              group=data_parallel_group,
          )
          dist.all_gather(
              gathered_text_features,
              text_features,
              group=data_parallel_group,
          )
          # TODO (yuya): check what's this
          if not local_loss:
              # ensure grads for local rank when all_* features don't have a gradient
              # https://amsword.medium.com/gradient-backpropagation-with-torch-distributed-all-gather-9f3941a381f8
              gathered_image_features[
                  data_parallel_rank
              ] = image_features
              gathered_text_features[data_parallel_rank] = text_features
          all_image_features = torch.cat(gathered_image_features, dim=0)
          all_text_features = torch.cat(gathered_text_features, dim=0)
  
      return all_image_features, all_text_features
  
file_path: NeMo/nemo/collections/multimodal/losses/clip_loss.py
imports: import torch
  import torch.distributed.nn
  import torch.nn
  from torch import distributed
  from torch.nn import functional
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group
label_desc: Use image node_id 1 for calling a global function with example usage: gather_features(image_features, text_features, local_loss, gather_with_grad) and returns: all_image_features,  all_text_features