neulab/docprompting-conala huggingface dataset:
tf.raw_ops.BatchNormWithGlobalNormalizationGrad Gradients for batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BatchNormWithGlobalNormalizationGrad  
tf.raw_ops.BatchNormWithGlobalNormalizationGrad(
    t, m, v, gamma, backprop, variance_epsilon, scale_after_normalization, name=None
)
 This op is deprecated. See tf.nn.batch_normalization.
 


 Args
  t   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A 4D input Tensor.  
  m   A Tensor. Must have the same type as t. A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.  
  v   A Tensor. Must have the same type as t. A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.  
  gamma   A Tensor. Must have the same type as t. A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this Tensor will be multiplied with the normalized Tensor.  
  backprop   A Tensor. Must have the same type as t. 4D backprop Tensor.  
  variance_epsilon   A float. A small float number to avoid dividing by 0.  
  scale_after_normalization   A bool. A bool indicating whether the resulted tensor needs to be multiplied with gamma.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (dx, dm, dv, db, dg).     dx   A Tensor. Has the same type as t.  
  dm   A Tensor. Has the same type as t.  
  dv   A Tensor. Has the same type as t.  
  db   A Tensor. Has the same type as t.  
  dg   A Tensor. Has the same type as t.
tf.raw_ops.BatchSelfAdjointEig  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BatchSelfAdjointEig  
tf.raw_ops.BatchSelfAdjointEig(
    input, name=None
)

 


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


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.BatchSelfAdjointEigV2  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BatchSelfAdjointEigV2  
tf.raw_ops.BatchSelfAdjointEigV2(
    input, compute_v=True, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32.  
  compute_v   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (e, v).     e   A Tensor. Has the same type as input.  
  v   A Tensor. Has the same type as input.
tf.raw_ops.BatchSvd  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BatchSvd  
tf.raw_ops.BatchSvd(
    input, compute_uv=True, full_matrices=False, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float64, float32, complex64, complex128.  
  compute_uv   An optional bool. Defaults to True.  
  full_matrices   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (s, u, v).     s   A Tensor. Has the same type as input.  
  u   A Tensor. Has the same type as input.  
  v   A Tensor. Has the same type as input.
tf.raw_ops.BatchToSpace BatchToSpace for 4-D tensors of type T.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BatchToSpace  
tf.raw_ops.BatchToSpace(
    input, crops, block_size, name=None
)
 This is a legacy version of the more general BatchToSpaceND. Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the batch dimension are moved in spatial blocks to the height and width dimensions, followed by cropping along the height and width dimensions.
 


 Args
  input   A Tensor. 4-D tensor with shape [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth]. Note that the batch size of the input tensor must be divisible by block_size * block_size.  
  crops   A Tensor. Must be one of the following types: int32, int64. 2-D tensor of non-negative integers with shape [2, 2]. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows: crops = [[crop_top, crop_bottom], [crop_left, crop_right]] 
 
  block_size   An int that is >= 2.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.BesselI0  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselI0  
tf.raw_ops.BesselI0(
    x, name=None
)

 


 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.BesselI0e  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselI0e  
tf.raw_ops.BesselI0e(
    x, name=None
)

 


 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.BesselI1  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselI1  
tf.raw_ops.BesselI1(
    x, name=None
)

 


 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.BesselI1e  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselI1e  
tf.raw_ops.BesselI1e(
    x, name=None
)

 


 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.BesselJ0  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselJ0  
tf.raw_ops.BesselJ0(
    x, name=None
)

 


 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.BesselJ1  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselJ1  
tf.raw_ops.BesselJ1(
    x, name=None
)

 


 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.BesselK0  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselK0  
tf.raw_ops.BesselK0(
    x, name=None
)

 


 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.BesselK0e  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselK0e  
tf.raw_ops.BesselK0e(
    x, name=None
)

 


 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.BesselK1  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselK1  
tf.raw_ops.BesselK1(
    x, name=None
)

 


 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.BesselK1e  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselK1e  
tf.raw_ops.BesselK1e(
    x, name=None
)

 


 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.BesselY0  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselY0  
tf.raw_ops.BesselY0(
    x, name=None
)

 


 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.BesselY1  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BesselY1  
tf.raw_ops.BesselY1(
    x, name=None
)

 


 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.Betainc   Compute the regularized incomplete beta integral \(I_x(a, b)\).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Betainc  
tf.raw_ops.Betainc(
    a, b, x, name=None
)
 The regularized incomplete beta integral is defined as: \(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\) where \(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\) is the incomplete beta function and \(B(a, b)\) is the complete beta function.
 


 Args
  a   A Tensor. Must be one of the following types: float32, float64.  
  b   A Tensor. Must have the same type as a.  
  x   A Tensor. Must have the same type as a.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as a.
tf.raw_ops.BiasAdd Adds bias to value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BiasAdd  
tf.raw_ops.BiasAdd(
    value, bias, data_format='NHWC', name=None
)
 This is a special case of tf.add where bias is restricted to be 1-D. Broadcasting is supported, so value may have any number of dimensions.
 


 Args
  value   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Any number of dimensions.  
  bias   A Tensor. Must have the same type as value. 1-D with size the last dimension of value.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. The tensor will be added to "in_channels", the third-to-the-last dimension.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as value.
tf.raw_ops.BiasAddGrad The backward operation for "BiasAdd" on the "bias" tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BiasAddGrad  
tf.raw_ops.BiasAddGrad(
    out_backprop, data_format='NHWC', name=None
)
 It accumulates all the values from out_backprop into the feature dimension. For NHWC data format, the feature dimension is the last. For NCHW data format, the feature dimension is the third-to-last.
 


 Args
  out_backprop   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Any number of dimensions.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. The tensor will be added to "in_channels", the third-to-the-last dimension.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as out_backprop.
tf.raw_ops.BiasAddV1 Adds bias to value.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BiasAddV1  
tf.raw_ops.BiasAddV1(
    value, bias, name=None
)
 This is a deprecated version of BiasAdd and will be soon removed. This is a special case of tf.add where bias is restricted to be 1-D. Broadcasting is supported, so value may have any number of dimensions.
 


 Args
  value   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Any number of dimensions.  
  bias   A Tensor. Must have the same type as value. 1-D with size the last dimension of value.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as value.
tf.raw_ops.Bincount Counts the number of occurrences of each value in an integer array.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Bincount  
tf.raw_ops.Bincount(
    arr, size, weights, name=None
)
 Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i. Values in arr outside of the range [0, size) are ignored.
 


 Args
  arr   A Tensor of type int32. int32 Tensor.  
  size   A Tensor of type int32. non-negative int32 scalar Tensor.  
  weights   A Tensor. Must be one of the following types: int32, int64, float32, float64. is an int32, int64, float32, or float64 Tensor with the same shape as arr, or a length-0 Tensor, in which case it acts as all weights equal to 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as weights.
tf.raw_ops.Bitcast Bitcasts a tensor from one type to another without copying data.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Bitcast  
tf.raw_ops.Bitcast(
    input, type, name=None
)
 Given a tensor input, this operation returns a tensor that has the same buffer data as input with datatype type. If the input datatype T is larger than the output datatype type then the shape changes from [...] to [..., sizeof(T)/sizeof(type)]. If T is smaller than type, the operator requires that the rightmost dimension be equal to sizeof(type)/sizeof(T). The shape then goes from [..., sizeof(type)/sizeof(T)] to [...]. tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() gives module error. For example, Example 1: 
a = [1., 2., 3.]
equality_bitcast = tf.bitcast(a, tf.complex128)
Traceback (most recent call last):

InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast]
equality_cast = tf.cast(a, tf.complex128)
print(equality_cast)
tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)
 Example 2: 
tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
<tf.Tensor: shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>
 Example 3: 
x = [1., 2., 3.]
y = [0., 2., 3.]
equality= tf.equal(x,y)
equality_cast = tf.cast(equality,tf.float32)
equality_bitcast = tf.bitcast(equality_cast,tf.uint8)
print(equality)
tf.Tensor([False True True], shape=(3,), dtype=bool)
print(equality_cast)
tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
print(equality_bitcast)
tf.Tensor(
    [[  0   0   0   0]
     [  0   0 128  63]
     [  0   0 128  63]], shape=(3, 4), dtype=uint8)
 
Note: Bitcast is implemented as a low-level cast, so machines with different endian orderings will give different results.

 


 Args
  input   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.  
  type   A tf.DType from: tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type type.
tf.raw_ops.BitwiseAnd Elementwise computes the bitwise AND of x and y.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BitwiseAnd  
tf.raw_ops.BitwiseAnd(
    x, y, name=None
)
 The result will have those bits set, that are set in both x and y. The computation is performed on the underlying representations of x and y. For example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
              tf.uint8, tf.uint16, tf.uint32, tf.uint64]

for dtype in dtype_list:
  lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
  exp = tf.constant([0, 0, 3, 10], dtype=tf.float32)

  res = bitwise_ops.bitwise_and(lhs, rhs)
  tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE

 


 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.BitwiseOr Elementwise computes the bitwise OR of x and y.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BitwiseOr  
tf.raw_ops.BitwiseOr(
    x, y, name=None
)
 The result will have those bits set, that are set in x, y or both. The computation is performed on the underlying representations of x and y. For example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
              tf.uint8, tf.uint16, tf.uint32, tf.uint64]

for dtype in dtype_list:
  lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
  exp = tf.constant([5, 5, 7, 15], dtype=tf.float32)

  res = bitwise_ops.bitwise_or(lhs, rhs)
  tf.assert_equal(tf.cast(res,  tf.float32), exp)  # TRUE

 


 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.BitwiseXor Elementwise computes the bitwise XOR of x and y.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BitwiseXor  
tf.raw_ops.BitwiseXor(
    x, y, name=None
)
 The result will have those bits set, that are different in x and y. The computation is performed on the underlying representations of x and y. For example: import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
              tf.uint8, tf.uint16, tf.uint32, tf.uint64]

for dtype in dtype_list:
  lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
  exp = tf.constant([5, 5, 4, 5],  dtype=tf.float32)

  res = bitwise_ops.bitwise_xor(lhs, rhs)
  tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE

 


 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.BlockLSTM Computes the LSTM cell forward propagation for all the time steps.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.BlockLSTM  
tf.raw_ops.BlockLSTM(
    seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1,
    cell_clip=3, use_peephole=False, name=None
)
 This is equivalent to applying LSTMBlockCell in a loop, like so: for x1 in unpack(x):
  i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
    x1, cs_prev, h_prev, w, wci, wcf, wco, b)
  cs_prev = cs1
  h_prev = h1
  i.append(i1)
  cs.append(cs1)
  f.append(f1)
  o.append(o1)
  ci.append(ci1)
  co.append(co1)
  h.append(h1)
return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)

 


 Args
  seq_len_max   A Tensor of type int64. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.  
  x   A Tensor. Must be one of the following types: half, float32. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).  
  cs_prev   A Tensor. Must have the same type as x. Value of the initial cell state.  
  h_prev   A Tensor. Must have the same type as x. Initial output of cell (to be used for peephole).  
  w   A Tensor. Must have the same type as x. The weight matrix.  
  wci   A Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.  
  wcf   A Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.  
  wco   A Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.  
  b   A Tensor. Must have the same type as x. The bias vector.  
  forget_bias   An optional float. Defaults to 1. The forget gate bias.  
  cell_clip   An optional float. Defaults to 3. Value to clip the 'cs' value to.  
  use_peephole   An optional bool. Defaults to False. Whether to use peephole weights.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (i, cs, f, o, ci, co, h).     i   A Tensor. Has the same type as x.  
  cs   A Tensor. Has the same type as x.  
  f   A Tensor. Has the same type as x.  
  o   A Tensor. Has the same type as x.  
  ci   A Tensor. Has the same type as x.  
  co   A Tensor. Has the same type as x.  
  h   A Tensor. Has the same type as x.