class_docstr: Core Embedding Column.
class_docstr_tok: ["Core","Embedding","Column","."]
code: def _get_dense_tensor(
      self, inputs, weight_collections=None, trainable=None
  ):
      if tpu.under_tpu_inference_context():
  
          def host_computation():
              return fc._EmbeddingColumn._get_dense_tensor(
                  self, inputs, weight_collections, trainable
              )
  
          return tpu_replication.outside_compilation(host_computation)
  
      if _is_running_on_cpu():
          return fc._EmbeddingColumn._get_dense_tensor(
              self, inputs, weight_collections, trainable
          )
  
      # TPU mode
      # Get the embeddings from the LazyBuilder.
      tensor = inputs.get(self.get_feature_key_name())
  
      # 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 tensor
  
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 10 for calling the _TPUEmbeddingColumn obj's underlying member method code with example usage: obj._get_dense_tensor(inputs, weight_collections, trainable) and returns: tensor, tpu_replication, fc, fc