class_docstr: Parameter to represent a slice of a table batched embedding. The slice will be
  a view of the TBE of shape (num_embeddings, embedding_dim) and contain consistent .grad
  
  unlike nn.Parameter, requires_grad is not present and follows requires_grad of TBE.data
  
  Args:
      data (torch.Tensor): original Data (of a TBE) to make a slice of
      start_offset (int):
      end_offset (int):
      num_embeddings (int):
      embedding_dim (int):
class_docstr_tok: ["Parameter","to","represent","a","slice","of","a","table","batched","embedding",".","The","slice","will","be","a","view","of","the","TBE","of","shape","(","num_embeddings",",","embedding_dim",")","and","contain","consistent",".grad","unlike","nn.Parameter",",","requires_grad","is","not","present","and","follows","requires_grad","of","TBE.data","Args",":","data","(","torch.Tensor",")",":","original","Data","(","of","a","TBE",")","to","make","a","slice","of","start_offset","(","int",")",":","end_offset","(","int",")",":","num_embeddings","(","int",")",":","embedding_dim","(","int",")",":"]
code: def __new__(
      cls,
      data: torch.Tensor,
      start_offset: int,
      end_offset: int,
      num_embeddings: int,
      embedding_dim: int,
  ) -> "TableBatchedEmbeddingSlice":
      _slice = data[start_offset:end_offset].view(
          num_embeddings, embedding_dim
      )
      return _slice.as_subclass(cls)
  
file_path: torchrec/torchrec/distributed/composable/table_batched_embedding_slice.py
imports: from typing import Dict, Optional
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the TableBatchedEmbeddingSlice obj's underlying member method code with example usage: obj.__new__(cls, data, start_offset, end_offset, num_embeddings, embedding_dim) and returns: _slice