class_docstr: This is the loss which was proposed in SimCLR https://arxiv.org/abs/2002.05709 paper.
  See the paper for the details on the loss.
  
  Config params:
      temperature (float): the temperature to be applied on the logits
      buffer_params:
          world_size (int): total number of trainers in training
          embedding_dim (int): output dimensions of the features projects
          effective_batch_size (int): total batch size used (includes positives)
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docstr: Instantiates SimclrInfoNCELoss from configuration.
  
  Args:
      loss_config: configuration for the loss
  
  Returns:
      SimclrInfoNCELoss instance.
docstr_tok: ["Instantiates","SimclrInfoNCELoss","from","configuration",".","Args",":","loss_config",":","configuration","for","the","loss","Returns",":","SimclrInfoNCELoss","instance","."]
code: def from_config(cls, loss_config: AttrDict):
      """
      Instantiates SimclrInfoNCELoss from configuration.
  
      Args:
          loss_config: configuration for the loss
  
      Returns:
          SimclrInfoNCELoss instance.
      """
      return cls(loss_config)
  
imports: import logging
  import pprint
  import numpy
  import torch
  from classy_vision.generic.distributed_util import get_cuda_device_index, get_rank
  from classy_vision.losses import ClassyLoss, register_loss
  from torch import nn
  from vissl.config import AttrDict
  from vissl.utils.distributed_utils import gather_from_all
label_desc: Use image node_id 2 for calling the SimclrInfoNCELoss obj's underlying member method code with example usage: obj.from_config(cls, loss_config) and returns: cls