class_docstr: Core Embedding Column.
class_docstr_tok: ["Core","Embedding","Column","."]
code: def _get_sequence_dense_tensor(
      self, inputs, weight_collections=None, trainable=None
  ):
      if tpu.under_tpu_inference_context():
  
          def host_computation():
              return fc._EmbeddingColumn._get_sequence_dense_tensor(
                  self, inputs, weight_collections, trainable
              )
  
          return tpu_replication.outside_compilation(host_computation)
  
      if _is_running_on_cpu():
          return fc._EmbeddingColumn._get_sequence_dense_tensor(
              self, inputs, weight_collections, trainable
          )
  
      tensor = inputs.get(self.get_feature_key_name())
      tensor_lengths = inputs.get(
          self.get_sequence_length_feature_key_name()
      )
  
      # inputs is a _LazyBuilder and for rank 1 tensors, it calls expand_dims(-1).
      # We need to undo this to match the standard CPU sequence embedding.
      tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
  
      # Add to collection for _create_tpu_embedding_variables_and_ops
      _record_variable_scope_and_name(
          self.get_embedding_var_name(),
          "embedding_weights",
          bypass_scope_validation=self._bypass_scope_validation,
      )
  
      return fc._SequenceDenseColumn.TensorSequenceLengthPair(
          dense_tensor=tensor, sequence_length=tensor_lengths
      )
  
imports: import math
  from tensorflow.python.feature_column import feature_column
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.tpu import tpu
  from tensorflow.python.tpu import tpu_function
  from tensorflow.python.tpu import tpu_replication
label_desc: Use image node_id 11 for calling the _TPUEmbeddingColumn obj's underlying member method code with example usage: obj._get_sequence_dense_tensor(inputs, weight_collections, trainable) and returns: fc, tpu_replication, fc, fc