class_docstr: Glow TTS models from https://arxiv.org/abs/2005.11129
  
  Args:
      num_chars (int): number of embedding characters.
      hidden_channels_enc (int): number of embedding and encoder channels.
      hidden_channels_dec (int): number of decoder channels.
      use_encoder_prenet (bool): enable/disable prenet for encoder. Prenet modules are hard-coded for each alternative encoder.
      hidden_channels_dp (int): number of duration predictor channels.
      out_channels (int): number of output channels. It should be equal to the number of spectrogram filter.
      num_flow_blocks_dec (int): number of decoder blocks.
      kernel_size_dec (int): decoder kernel size.
      dilation_rate (int): rate to increase dilation by each layer in a decoder block.
      num_block_layers (int): number of decoder layers in each decoder block.
      dropout_p_dec (float): dropout rate for decoder.
      num_speaker (int): number of speaker to define the size of speaker embedding layer.
      c_in_channels (int): number of speaker embedding channels. It is set to 512 if embeddings are learned.
      num_splits (int): number of split levels in inversible conv1x1 operation.
      num_squeeze (int): number of squeeze levels. When squeezing channels increases and time steps reduces by the factor 'num_squeeze'.
      sigmoid_scale (bool): enable/disable sigmoid scaling in decoder.
      mean_only (bool): if True, encoder only computes mean value and uses constant variance for each time step.
      encoder_type (str): encoder module type.
      encoder_params (dict): encoder module parameters.
      external_speaker_embedding_dim (int): channels of external speaker embedding vectors.
class_docstr_tok: ["Glow","TTS","models","from","https",":","\/\/arxiv.org\/abs\/2005.11129","Args",":","num_chars","(","int",")",":","number","of","embedding","characters",".","hidden_channels_enc","(","int",")",":","number","of","embedding","and","encoder","channels",".","hidden_channels_dec","(","int",")",":","number","of","decoder","channels",".","use_encoder_prenet","(","bool",")",":","enable\/disable","prenet","for","encoder",".","Prenet","modules","are","hard-coded","for","each","alternative","encoder",".","hidden_channels_dp","(","int",")",":","number","of","duration","predictor","channels",".","out_channels","(","int",")",":","number","of","output","channels",".","It","should","be","equal","to","the","number","of","spectrogram","filter",".","num_flow_blocks_dec","(","int",")",":","number","of","decoder","blocks",".","kernel_size_dec","(","int",")",":","decoder","kernel","size",".","dilation_rate","(","int",")",":","rate","to","increase","dilation","by","each","layer","in","a","decoder","block",".","num_block_layers","(","int",")",":","number","of","decoder","layers","in","each","decoder","block",".","dropout_p_dec","(","float",")",":","dropout","rate","for","decoder",".","num_speaker","(","int",")",":","number","of","speaker","to","define","the","size","of","speaker","embedding","layer",".","c_in_channels","(","int",")",":","number","of","speaker","embedding","channels",".","It","is","set","to","512","if","embeddings","are","learned",".","num_splits","(","int",")",":","number","of","split","levels","in","inversible","conv1x1","operation",".","num_squeeze","(","int",")",":","number","of","squeeze","levels",".","When","squeezing","channels","increases","and","time","steps","reduces","by","the","factor","'num_squeeze","'",".","sigmoid_scale","(","bool",")",":","enable\/disable","sigmoid","scaling","in","decoder",".","mean_only","(","bool",")",":","if","True",",","encoder","only","computes","mean","value","and","uses","constant","variance","for","each","time","step",".","encoder_type","(","str",")",":","encoder","module","type",".","encoder_params","(","dict",")",":","encoder","module","parameters",".","external_speaker_embedding_dim","(","int",")",":","channels","of","external","speaker","embedding","vectors","."]
code: def load_checkpoint(
      self, config, checkpoint_path, eval=False
  ):  # pylint: disable=unused-argument, redefined-builtin
      state = torch.load(
          checkpoint_path, map_location=torch.device("cpu")
      )
      self.load_state_dict(state["model"])
      if eval:
          self.eval()
          self.store_inverse()
          assert not self.training
  
file_path: TTS/TTS/tts/models/glow_tts.py
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.glow_tts.encoder import Encoder
  from TTS.tts.layers.glow_tts.decoder import Decoder
  from TTS.tts.utils.generic_utils import sequence_mask
  from TTS.tts.layers.glow_tts.monotonic_align import maximum_path, generate_path
label_desc: Use image node_id 7 for calling the GlowTts obj's underlying member method code with example usage: obj.load_checkpoint(config, checkpoint_path, eval) without return types