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Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: The code provides Python wrappers around TensorFlow ops for decoding LibSVM input to tensors. To use it, call the `decode_libsvm` function with the following arguments: `input` (a `Tensor` of type `string`), `num_features` (an `int` that is `>= 1`), `dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`), `label_dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`), and `name` (a name for the operation, optional). The function returns a tuple of `Tensor` objects `(label, feature_indices, feature_values, feature_shape)`, where `label` is a `Tensor` of type `label_dtype`, `feature_indices` is a `Tensor` of type `int64`, `feature_values` is a `Tensor` of type `dtype`, and `feature_shape` is a `Tensor` of type `int64`. ### Input: ### Output: """Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. Original C++ source file: libsvm_ops.cc """ import collections as _collections import six as _six from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 # Needed to trigger the call to _set_call_cpp_shape_fn. from tensorflow.python.framework import common_shapes as _common_shapes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.tf_export import tf_export _decode_libsvm_outputs = ["label", "feature_indices", "feature_values", "feature_shape"] _DecodeLibsvmOutput = _collections.namedtuple( "DecodeLibsvm", _decode_libsvm_outputs) @tf_export('decode_libsvm') def decode_libsvm(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None): r"""Convert LibSVM input to tensors. The output consists of a label and a feature tensor. The shape of the label tensor is the same as input and the shape of the feature tensor is `[input_shape, num_features]`. Args: input: A `Tensor` of type `string`. Each string is a record in the LibSVM. num_features: An `int` that is `>= 1`. The number of features. dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`. label_dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (label, feature_indices, feature_values, feature_shape). label: A `Tensor` of type `label_dtype`. A tensor of the same shape as input. feature_indices: A `Tensor` of type `int64`. A 2-D int64 tensor of dense_shape [N, ndims]. feature_values: A `Tensor` of type `dtype`. A 1-D tensor of any type and dense_shape [N]. feature_shape: A `Tensor` of type `int64`. A 1-D int64 tensor of dense_shape [ndims]. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: num_features = _execute.make_int(num_features, "num_features") if dtype is None: dtype = _dtypes.float32 dtype = _execute.make_type(dtype, "dtype") if label_dtype is None: label_dtype = _dtypes.int64 label_dtype = _execute.make_type(label_dtype, "label_dtype") _, _, _op = _op_def_lib._apply_op_helper( "DecodeLibsvm", input=input, num_features=num_features, dtype=dtype, label_dtype=label_dtype, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("dtype", _op.get_attr("dtype"), "label_dtype", _op.get_attr("label_dtype"), "num_features", _op.get_attr("num_features")) _execute.record_gradient( "DecodeLibsvm", _inputs_flat, _attrs, _result, name) _result = _DecodeLibsvmOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "DecodeLibsvm", name, _ctx._post_execution_callbacks, input, "dtype", dtype, "label_dtype", label_dtype, "num_features", num_features) _result = _DecodeLibsvmOutput._make(_result) return _result except _core._FallbackException: return decode_libsvm_eager_fallback( input, dtype=dtype, label_dtype=label_dtype, num_features=num_features, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def decode_libsvm_eager_fallback(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function decode_libsvm """ _ctx = ctx if ctx else _context.context() num_features = _execute.make_int(num_features, "num_features") if dtype is None: dtype = _dtypes.float32 dtype = _execute.make_type(dtype, "dtype") if label_dtype is None: label_dtype = _dtypes.int64 label_dtype = _execute.make_type(label_dtype, "label_dtype") input = _ops.convert_to_tensor(input, _dtypes.string) _inputs_flat = [input] _attrs = ("dtype", dtype, "label_dtype", label_dtype, "num_features", num_features) _result = _execute.execute(b"DecodeLibsvm", 4, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "DecodeLibsvm", _inputs_flat, _attrs, _result, name) _result = _DecodeLibsvmOutput._make(_result) return _result _ops.RegisterShape("DecodeLibsvm")(None) def _InitOpDefLibrary(op_list_proto_bytes): op_list = _op_def_pb2.OpList() op_list.ParseFromString(op_list_proto_bytes) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary() op_def_lib.add_op_list(op_list) return op_def_lib # op { # name: "DecodeLibsvm" # input_arg { # name: "input" # type: DT_STRING # } # output_arg { # name: "label" # type_attr: "label_dtype" # } # output_arg { # name: "feature_indices" # type: DT_INT64 # } # output_arg { # name: "feature_values" # type_attr: "dtype" # } # output_arg { # name: "feature_shape" # type: DT_INT64 # } # attr { # name: "dtype" # type: "type" # default_value { # type: DT_FLOAT # } # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_INT64 # } # } # } # attr { # name: "label_dtype" # type: "type" # default_value { # type: DT_INT64 # } # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_INT64 # } # } # } # attr { # name: "num_features" # type: "int" # has_minimum: true # minimum: 1 # } # } _op_def_lib = _InitOpDefLibrary(b"\n\311\001\n\014DecodeLibsvm\022\t\n\005input\030\007\032\024\n\005label\"\013label_dtype\032\023\n\017feature_indices\030\t\032\027\n\016feature_values\"\005dtype\032\021\n\rfeature_shape\030\t\"\033\n\005dtype\022\004type\032\0020\001:\010\n\0062\004\001\002\003\t\"!\n\013label_dtype\022\004type\032\0020\t:\010\n\0062\004\001\002\003\t\"\027\n\014num_features\022\003int(\0010\001")

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