class_docstr: An abstract RNNT Joint framework, which can possibly integrate with GreedyRNNTInfer and BeamRNNTInfer classes.
  Represents the abstract RNNT Joint network, which accepts the acoustic model and prediction network
  embeddings in order to compute the joint of the two prior to decoding the output sequence.
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docstr: Compute the joint step of the network after the projection step.
  Args:
      f: Output of the Encoder model after projection. A torch.Tensor of shape [B, T, H]
      g: Output of the Decoder model (Prediction Network) after projection. A torch.Tensor of shape [B, U, H]
  
  Returns:
      Logits / log softmaxed tensor of shape (B, T, U, V + 1).
      Arbitrary return type, preferably torch.Tensor, but not limited to (e.g., see HatJoint)
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code: def joint_after_projection(
      self, f: torch.Tensor, g: torch.Tensor
  ) -> Any:
      """
      Compute the joint step of the network after the projection step.
      Args:
          f: Output of the Encoder model after projection. A torch.Tensor of shape [B, T, H]
          g: Output of the Decoder model (Prediction Network) after projection. A torch.Tensor of shape [B, U, H]
  
      Returns:
          Logits / log softmaxed tensor of shape (B, T, U, V + 1).
          Arbitrary return type, preferably torch.Tensor, but not limited to (e.g., see HatJoint)
      """
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.core import NeuralModule
label_desc: Use image node_id 1 for calling the AbstractRNNTJoint obj's underlying member method code with example usage: obj.joint_after_projection(f, g) without return types