class_docstr: Module for alignment text and mel spectrogram.
  
  Args:
      n_mel_channels: Dimension of mel spectrogram.
      n_text_channels: Dimension of text embeddings.
      n_att_channels: Dimension of model
      temperature: Temperature to scale distance by.
          Suggested to be 0.0005 when using dist_type "l2" and 15.0 when using "cosine".
      condition_types: List of types for nemo.collections.tts.modules.submodules.ConditionalInput.
      dist_type: Distance type to use for similarity measurement. Supports "l2" and "cosine" distance.
class_docstr_tok: ["Module","for","alignment","text","and","mel","spectrogram",".","Args",":","n_mel_channels",":","Dimension","of","mel","spectrogram",".","n_text_channels",":","Dimension","of","text","embeddings",".","n_att_channels",":","Dimension","of","model","temperature",":","Temperature","to","scale","distance","by",".","Suggested","to","be","0.0005","when","using","dist_type","``","l2","''","and","15.0","when","using","``","cosine","''",".","condition_types",":","List","of","types","for","nemo.collections.tts.modules.submodules.ConditionalInput",".","dist_type",":","Distance","type","to","use","for","similarity","measurement",".","Supports","``","l2","''","and","``","cosine","''","distance","."]
code: def get_cosine_dist(queries_enc, keys_enc):
      queries_enc = rearrange(queries_enc, "B C T1 -> B C T1 1")
      keys_enc = rearrange(keys_enc, "B C T2 -> B C 1 T2")
      cosine_dist = -torch.nn.functional.cosine_similarity(
          queries_enc, keys_enc, dim=1
      )
      cosine_dist = rearrange(cosine_dist, "B T1 T2 -> B 1 T1 T2")
      return cosine_dist
  
file_path: NeMo/nemo/collections/tts/modules/aligner.py
imports: import torch
  from einops import rearrange
  from torch import nn
  from nemo.collections.tts.modules.submodules import ConditionalInput, ConvNorm
  from nemo.collections.tts.parts.utils.helpers import binarize_attention_parallel
label_desc: Use image node_id 5 for calling the AlignmentEncoder obj's underlying member method code with example usage: obj.get_cosine_dist(queries_enc, keys_enc) and returns: cosine_dist