models.py
def forward(self, x_t):
		"""
		x_t : LongTensor of shape (batch size)

		Get observation probabilities
		"""
		# Each col needs to add up to 1 (in probability domain)
		emission_matrix = torch.nn.functional.log_softmax(self.unnormalized_emission_matrix, dim=1)
		out = emission_matrix[:, x_t].transpose(0,1)

		return out

		x_t : LongTensor of shape (batch size)

		Get observation probabilities
		
x_t : LongTensor of shape (batch size)
Get observation probabilities
x_t : LongTensor of shape ( batch size ) Get observation probabilities
def forward(self, x_t):
		emission_matrix = torch.nn.functional.log_softmax(self.unnormalized_emission_matrix, dim=1)
		out = emission_matrix[:, x_t].transpose(0,1)
		return out
def forward ( self , x_t ) : emission_matrix = torch . nn . functional . log_softmax ( self . unnormalized_emission_matrix , dim = 1 ) out = emission_matrix [ : , x_t ] . transpose ( 0 , 1 ) return out
x_t : LongTensor of shape (batch size)
Get observation probabilities
x_t : LongTensor of shape ( batch size ) Get observation probabilities
"""
		x_t : LongTensor of shape (batch size)

		Get observation probabilities
		""" # Each col needs to add up to 1 (in probability domain)
self
x_t