neulab/docprompting-conala huggingface dataset:
tf.raw_ops.Reverse Reverses specific dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Reverse  
tf.raw_ops.Reverse(
    tensor, dims, name=None
)
 Given a tensor, and a bool tensor dims representing the dimensions of tensor, this operation reverses each dimension i of tensor where dims[i] is True. tensor can have up to 8 dimensions. The number of dimensions of tensor must equal the number of elements in dims. In other words: rank(tensor) = size(dims) For example: # tensor 't' is [[[[ 0,  1,  2,  3],
#                  [ 4,  5,  6,  7],
#                  [ 8,  9, 10, 11]],
#                 [[12, 13, 14, 15],
#                  [16, 17, 18, 19],
#                  [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]

# 'dims' is [False, False, False, True]
reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
                        [ 7,  6,  5,  4],
                        [ 11, 10, 9, 8]],
                       [[15, 14, 13, 12],
                        [19, 18, 17, 16],
                        [23, 22, 21, 20]]]]

# 'dims' is [False, True, False, False]
reverse(t, dims) ==> [[[[12, 13, 14, 15],
                        [16, 17, 18, 19],
                        [20, 21, 22, 23]
                       [[ 0,  1,  2,  3],
                        [ 4,  5,  6,  7],
                        [ 8,  9, 10, 11]]]]

# 'dims' is [False, False, True, False]
reverse(t, dims) ==> [[[[8, 9, 10, 11],
                        [4, 5, 6, 7],
                        [0, 1, 2, 3]]
                       [[20, 21, 22, 23],
                        [16, 17, 18, 19],
                        [12, 13, 14, 15]]]]

 


 Args
  tensor   A Tensor. Must be one of the following types: uint8, int8, uint16, int16, int32, int64, bool, half, float32, float64, complex64, complex128, string. Up to 8-D.  
  dims   A Tensor of type bool. 1-D. The dimensions to reverse.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.ReverseSequence Reverses variable length slices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReverseSequence  
tf.raw_ops.ReverseSequence(
    input, seq_lengths, seq_dim, batch_dim=0, name=None
)
 This op first slices input along the dimension batch_dim, and for each slice i, reverses the first seq_lengths[i] elements along the dimension seq_dim. The elements of seq_lengths must obey seq_lengths[i] <= input.dims[seq_dim], and seq_lengths must be a vector of length input.dims[batch_dim]. The output slice i along dimension batch_dim is then given by input slice i, with the first seq_lengths[i] slices along dimension seq_dim reversed. For example: # Given this:
batch_dim = 0
seq_dim = 1
input.dims = (4, 8, ...)
seq_lengths = [7, 2, 3, 5]

# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]

# while entries past seq_lens are copied through:
output[0, 7:, :, ...] = input[0, 7:, :, ...]
output[1, 2:, :, ...] = input[1, 2:, :, ...]
output[2, 3:, :, ...] = input[2, 3:, :, ...]
output[3, 2:, :, ...] = input[3, 2:, :, ...]
 In contrast, if: # Given this:
batch_dim = 2
seq_dim = 0
input.dims = (8, ?, 4, ...)
seq_lengths = [7, 2, 3, 5]

# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]

# while entries past seq_lens are copied through:
output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]

 


 Args
  input   A Tensor. The input to reverse.  
  seq_lengths   A Tensor. Must be one of the following types: int32, int64. 1-D with length input.dims(batch_dim) and max(seq_lengths) <= input.dims(seq_dim)  
  seq_dim   An int. The dimension which is partially reversed.  
  batch_dim   An optional int. Defaults to 0. The dimension along which reversal is performed.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.ReverseV2 Reverses specific dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReverseV2  
tf.raw_ops.ReverseV2(
    tensor, axis, name=None
)
 NOTE tf.reverse has now changed behavior in preparation for 1.0. tf.reverse_v2 is currently an alias that will be deprecated before TF 1.0. Given a tensor, and a int32 tensor axis representing the set of dimensions of tensor to reverse. This operation reverses each dimension i for which there exists j s.t. axis[j] == i. tensor can have up to 8 dimensions. The number of dimensions specified in axis may be 0 or more entries. If an index is specified more than once, a InvalidArgument error is raised. For example: # tensor 't' is [[[[ 0,  1,  2,  3],
#                  [ 4,  5,  6,  7],
#                  [ 8,  9, 10, 11]],
#                 [[12, 13, 14, 15],
#                  [16, 17, 18, 19],
#                  [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]

# 'dims' is [3] or 'dims' is [-1]
reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
                        [ 7,  6,  5,  4],
                        [ 11, 10, 9, 8]],
                       [[15, 14, 13, 12],
                        [19, 18, 17, 16],
                        [23, 22, 21, 20]]]]

# 'dims' is '[1]' (or 'dims' is '[-3]')
reverse(t, dims) ==> [[[[12, 13, 14, 15],
                        [16, 17, 18, 19],
                        [20, 21, 22, 23]
                       [[ 0,  1,  2,  3],
                        [ 4,  5,  6,  7],
                        [ 8,  9, 10, 11]]]]

# 'dims' is '[2]' (or 'dims' is '[-2]')
reverse(t, dims) ==> [[[[8, 9, 10, 11],
                        [4, 5, 6, 7],
                        [0, 1, 2, 3]]
                       [[20, 21, 22, 23],
                        [16, 17, 18, 19],
                        [12, 13, 14, 15]]]]

 


 Args
  tensor   A Tensor. Must be one of the following types: uint8, int8, uint16, int16, int32, int64, bool, bfloat16, half, float32, float64, complex64, complex128, string. Up to 8-D.  
  axis   A Tensor. Must be one of the following types: int32, int64. 1-D. The indices of the dimensions to reverse. Must be in the range [-rank(tensor), rank(tensor)).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.RFFT Real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RFFT  
tf.raw_ops.RFFT(
    input, fft_length, Tcomplex=tf.dtypes.complex64, name=None
)
 Computes the 1-dimensional discrete Fourier transform of a real-valued signal over the inner-most dimension of input. Since the DFT of a real signal is Hermitian-symmetric, RFFT only returns the fft_length / 2 + 1 unique components of the FFT: the zero-frequency term, followed by the fft_length / 2 positive-frequency terms. Along the axis RFFT is computed on, if fft_length is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64. A float32 tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [1]. The FFT length.  
  Tcomplex   An optional tf.DType from: tf.complex64, tf.complex128. Defaults to tf.complex64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tcomplex.
tf.raw_ops.RFFT2D 2D real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RFFT2D  
tf.raw_ops.RFFT2D(
    input, fft_length, Tcomplex=tf.dtypes.complex64, name=None
)
 Computes the 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of input. Since the DFT of a real signal is Hermitian-symmetric, RFFT2D only returns the fft_length / 2 + 1 unique components of the FFT for the inner-most dimension of output: the zero-frequency term, followed by the fft_length / 2 positive-frequency terms. Along each axis RFFT2D is computed on, if fft_length is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64. A float32 tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [2]. The FFT length for each dimension.  
  Tcomplex   An optional tf.DType from: tf.complex64, tf.complex128. Defaults to tf.complex64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tcomplex.
tf.raw_ops.RFFT3D 3D real-valued fast Fourier transform.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RFFT3D  
tf.raw_ops.RFFT3D(
    input, fft_length, Tcomplex=tf.dtypes.complex64, name=None
)
 Computes the 3-dimensional discrete Fourier transform of a real-valued signal over the inner-most 3 dimensions of input. Since the DFT of a real signal is Hermitian-symmetric, RFFT3D only returns the fft_length / 2 + 1 unique components of the FFT for the inner-most dimension of output: the zero-frequency term, followed by the fft_length / 2 positive-frequency terms. Along each axis RFFT3D is computed on, if fft_length is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64. A float32 tensor.  
  fft_length   A Tensor of type int32. An int32 tensor of shape [3]. The FFT length for each dimension.  
  Tcomplex   An optional tf.DType from: tf.complex64, tf.complex128. Defaults to tf.complex64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type Tcomplex.
tf.raw_ops.RGBToHSV Converts one or more images from RGB to HSV.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RGBToHSV  
tf.raw_ops.RGBToHSV(
    images, name=None
)
 Outputs a tensor of the same shape as the images tensor, containing the HSV value of the pixels. The output is only well defined if the value in images are in [0,1]. output[..., 0] contains hue, output[..., 1] contains saturation, and output[..., 2] contains value. All HSV values are in [0,1]. A hue of 0 corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. Usage Example: 
blue_image = tf.stack([
   tf.zeros([5,5]),
   tf.zeros([5,5]),
   tf.ones([5,5])],
   axis=-1)
blue_hsv_image = tf.image.rgb_to_hsv(blue_image)
blue_hsv_image[0,0].numpy()
array([0.6666667, 1. , 1. ], dtype=float32)

 


 Args
  images   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 1-D or higher rank. RGB data to convert. Last dimension must be size 3.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.RightShift Elementwise computes the bitwise right-shift of x and y.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RightShift  
tf.raw_ops.RightShift(
    x, y, name=None
)
 Performs a logical shift for unsigned integer types, and an arithmetic shift for signed integer types. If y is negative, or greater than or equal to than the width of x in bits the result is implementation defined. Example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
import numpy as np
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]

for dtype in dtype_list:
  lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)

  right_shift_result = bitwise_ops.right_shift(lhs, rhs)

  print(right_shift_result)

# This will print:
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8)
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16)
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int32)
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int64)

lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
bitwise_ops.right_shift(lhs, rhs)
# <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>

 


 Args
  x   A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.  
  y   A Tensor. Must have the same type as x.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Rint Returns element-wise integer closest to x.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Rint  
tf.raw_ops.Rint(
    x, name=None
)
 If the result is midway between two representable values, the even representable is chosen. For example: rint(-1.5) ==> -2.0
rint(0.5000001) ==> 1.0
rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.RngReadAndSkip Advance the counter of a counter-based RNG.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RngReadAndSkip  
tf.raw_ops.RngReadAndSkip(
    resource, alg, delta, name=None
)
 The state of the RNG after rng_read_and_skip(n) will be the same as that after uniform([n]) (or any other distribution). The actual increment added to the counter is an unspecified implementation choice.
 


 Args
  resource   A Tensor of type resource. The handle of the resource variable that stores the state of the RNG.  
  alg   A Tensor of type int32. The RNG algorithm.  
  delta   A Tensor of type uint64. The amount of advancement.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int64.
tf.raw_ops.RngSkip Advance the counter of a counter-based RNG.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RngSkip  
tf.raw_ops.RngSkip(
    resource, algorithm, delta, name=None
)
 The state of the RNG after rng_skip(n) will be the same as that after stateful_uniform([n]) (or any other distribution). The actual increment added to the counter is an unspecified implementation detail.
 


 Args
  resource   A Tensor of type resource. The handle of the resource variable that stores the state of the RNG.  
  algorithm   A Tensor of type int64. The RNG algorithm.  
  delta   A Tensor of type int64. The amount of advancement.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Roll Rolls the elements of a tensor along an axis.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Roll  
tf.raw_ops.Roll(
    input, shift, axis, name=None
)
 The elements are shifted positively (towards larger indices) by the offset of shift along the dimension of axis. Negative shift values will shift elements in the opposite direction. Elements that roll passed the last position will wrap around to the first and vice versa. Multiple shifts along multiple axes may be specified. For example: # 't' is [0, 1, 2, 3, 4]
roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2]

# shifting along multiple dimensions
# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]]

# shifting along the same axis multiple times
# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]]

 


 Args
  input   A Tensor.  
  shift   A Tensor. Must be one of the following types: int32, int64. Dimension must be 0-D or 1-D. shift[i] specifies the number of places by which elements are shifted positively (towards larger indices) along the dimension specified by axis[i]. Negative shifts will roll the elements in the opposite direction.  
  axis   A Tensor. Must be one of the following types: int32, int64. Dimension must be 0-D or 1-D. axis[i] specifies the dimension that the shift shift[i] should occur. If the same axis is referenced more than once, the total shift for that axis will be the sum of all the shifts that belong to that axis.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Round Rounds the values of a tensor to the nearest integer, element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Round  
tf.raw_ops.Round(
    x, name=None
)
 Rounds half to even. Also known as bankers rounding. If you want to round according to the current system rounding mode use std::cint.
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Rsqrt   Computes reciprocal of square root of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Rsqrt  
tf.raw_ops.Rsqrt(
    x, name=None
)
 I.e., \(y = 1 / \sqrt{x}\).
 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.RsqrtGrad Computes the gradient for the rsqrt of x wrt its input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RsqrtGrad  
tf.raw_ops.RsqrtGrad(
    y, dy, name=None
)
 Specifically, grad = dy * -0.5 * y^3, where y = rsqrt(x), and dy is the corresponding input gradient.
 


 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  dy   A Tensor. Must have the same type as y.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as y.
tf.raw_ops.SamplingDataset Creates a dataset that takes a Bernoulli sample of the contents of another dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SamplingDataset  
tf.raw_ops.SamplingDataset(
    input_dataset, rate, seed, seed2, output_types, output_shapes, name=None
)
 There is no transformation in the tf.data Python API for creating this dataset. Instead, it is created as a result of the filter_with_random_uniform_fusion static optimization. Whether this optimization is performed is determined by the experimental_optimization.filter_with_random_uniform_fusion option of tf.data.Options.
 


 Args
  input_dataset   A Tensor of type variant.  
  rate   A Tensor of type float32. A scalar representing the sample rate. Each element of input_dataset is retained with this probability, independent of all other elements.  
  seed   A Tensor of type int64. A scalar representing seed of random number generator.  
  seed2   A Tensor of type int64. A scalar representing seed2 of random number generator.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.Save Saves the input tensors to disk.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Save  
tf.raw_ops.Save(
    filename, tensor_names, data, name=None
)
 The size of tensor_names must match the number of tensors in data. data[i] is written to filename with name tensor_names[i]. See also SaveSlices.
 


 Args
  filename   A Tensor of type string. Must have a single element. The name of the file to which we write the tensor.  
  tensor_names   A Tensor of type string. Shape [N]. The names of the tensors to be saved.  
  data   A list of Tensor objects. N tensors to save.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SaveDataset  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SaveDataset  
tf.raw_ops.SaveDataset(
    input_dataset, path, shard_func_other_args, shard_func,
    compression='', use_shard_func=True, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  path   A Tensor of type string.  
  shard_func_other_args   A list of Tensor objects.  
  shard_func   A function decorated with @Defun.  
  compression   An optional string. Defaults to "".  
  use_shard_func   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SaveSlices Saves input tensors slices to disk.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SaveSlices  
tf.raw_ops.SaveSlices(
    filename, tensor_names, shapes_and_slices, data, name=None
)
 This is like Save except that tensors can be listed in the saved file as being a slice of a larger tensor. shapes_and_slices specifies the shape of the larger tensor and the slice that this tensor covers. shapes_and_slices must have as many elements as tensor_names. Elements of the shapes_and_slices input must either be:  The empty string, in which case the corresponding tensor is saved normally. A string of the form dim0 dim1 ... dimN-1 slice-spec where the dimI are the dimensions of the larger tensor and slice-spec specifies what part is covered by the tensor to save.  slice-spec itself is a :-separated list: slice0:slice1:...:sliceN-1 where each sliceI is either:  The string - meaning that the slice covers all indices of this dimension 
start,length where start and length are integers. In that case the slice covers length indices starting at start.  See also Save.
 


 Args
  filename   A Tensor of type string. Must have a single element. The name of the file to which we write the tensor.  
  tensor_names   A Tensor of type string. Shape [N]. The names of the tensors to be saved.  
  shapes_and_slices   A Tensor of type string. Shape [N]. The shapes and slice specifications to use when saving the tensors.  
  data   A list of Tensor objects. N tensors to save.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.SaveV2 Saves tensors in V2 checkpoint format.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.SaveV2  
tf.raw_ops.SaveV2(
    prefix, tensor_names, shape_and_slices, tensors, name=None
)
 By default, saves the named tensors in full. If the caller wishes to save specific slices of full tensors, "shape_and_slices" should be non-empty strings and correspondingly well-formed.
 


 Args
  prefix   A Tensor of type string. Must have a single element. The prefix of the V2 checkpoint to which we write the tensors.  
  tensor_names   A Tensor of type string. shape {N}. The names of the tensors to be saved.  
  shape_and_slices   A Tensor of type string. shape {N}. The slice specs of the tensors to be saved. Empty strings indicate that they are non-partitioned tensors.  
  tensors   A list of Tensor objects. N tensors to save.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.