neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ResourceApplyRMSProp Update '*var' according to the RMSProp algorithm.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyRMSProp  
tf.raw_ops.ResourceApplyRMSProp(
    var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None
)
 Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  ms   A Tensor of type resource. Should be from a Variable().  
  mom   A Tensor of type resource. Should be from a Variable().  
  lr   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. Scaling factor. Must be a scalar.  
  rho   A Tensor. Must have the same type as lr. Decay rate. Must be a scalar.  
  momentum   A Tensor. Must have the same type as lr.  
  epsilon   A Tensor. Must have the same type as lr. Ridge term. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceConditionalAccumulator A conditional accumulator for aggregating gradients.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceConditionalAccumulator  
tf.raw_ops.ResourceConditionalAccumulator(
    dtype, shape, container='', shared_name='',
    reduction_type='MEAN', name=None
)
 The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator. This is a resource version of ConditionalAccumulator that will work in TF2.0 with tf.cond version 2.
 


 Args
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The type of the value being accumulated.  
  shape   A tf.TensorShape or list of ints. The shape of the values, can be [], in which case shape is unknown.  
  container   An optional string. Defaults to "". If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used.  
  shared_name   An optional string. Defaults to "". If non-empty, this accumulator will be shared under the given name across multiple sessions.  
  reduction_type   An optional string from: "MEAN", "SUM". Defaults to "MEAN".  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type resource.
tf.raw_ops.ResourceCountUpTo Increments variable pointed to by 'resource' until it reaches 'limit'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceCountUpTo  
tf.raw_ops.ResourceCountUpTo(
    resource, limit, T, name=None
)

 


 Args
  resource   A Tensor of type resource. Should be from a scalar Variable node.  
  limit   An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.  
  T   A tf.DType from: tf.int32, tf.int64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type T.
tf.raw_ops.ResourceGather Gather slices from the variable pointed to by resource according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceGather  
tf.raw_ops.ResourceGather(
    resource, indices, dtype, batch_dims=0, validate_indices=True, name=None
)
 indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape indices.shape + params.shape[1:] where: # Scalar indices
output[:, ..., :] = params[indices, :, ... :]

# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]

# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]

 


 Args
  resource   A Tensor of type resource.  
  indices   A Tensor. Must be one of the following types: int32, int64.  
  dtype   A tf.DType.  
  batch_dims   An optional int. Defaults to 0.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.ResourceGatherNd  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceGatherNd  
tf.raw_ops.ResourceGatherNd(
    resource, indices, dtype, name=None
)

 


 Args
  resource   A Tensor of type resource.  
  indices   A Tensor. Must be one of the following types: int32, int64.  
  dtype   A tf.DType.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.ResourceScatterAdd Adds sparse updates to the variable referenced by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterAdd  
tf.raw_ops.ResourceScatterAdd(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] += updates[...]

# Vector indices (for each i)
ref[indices[i], ...] += updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterDiv Divides sparse updates into the variable referenced by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterDiv  
tf.raw_ops.ResourceScatterDiv(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] /= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] /= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterMax Reduces sparse updates into the variable referenced by resource using the max operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMax  
tf.raw_ops.ResourceScatterMax(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions are combined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterMin Reduces sparse updates into the variable referenced by resource using the min operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMin  
tf.raw_ops.ResourceScatterMin(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions are combined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterMul Multiplies sparse updates into the variable referenced by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMul  
tf.raw_ops.ResourceScatterMul(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] *= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] *= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterNdAdd Applies sparse addition to individual values or slices in a Variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdAdd  
tf.raw_ops.ResourceScatterNdAdd(
    ref, indices, updates, use_locking=True, name=None
)
 ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(add)
 The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values to add to ref.  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterNdMax  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdMax  
tf.raw_ops.ResourceScatterNdMax(
    ref, indices, updates, use_locking=True, name=None
)

 


 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values whose element wise max is taken with ref  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterNdMin  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdMin  
tf.raw_ops.ResourceScatterNdMin(
    ref, indices, updates, use_locking=True, name=None
)

 


 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values whose element wise min is taken with ref.  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterNdSub Applies sparse subtraction to individual values or slices in a Variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdSub  
tf.raw_ops.ResourceScatterNdSub(
    ref, indices, updates, use_locking=True, name=None
)
 ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that subtraction would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
sub = tf.scatter_nd_sub(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(sub)
 The resulting update to ref would look like this: [1, -9, 3, -6, -4, 6, 7, -4]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values to add to ref.  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterNdUpdate Applies sparse updates to individual values or slices within a given  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdUpdate  
tf.raw_ops.ResourceScatterNdUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 variable according to indices. ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
 For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(update)
 The resulting update to ref would look like this: [1, 11, 3, 10, 9, 6, 7, 12]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterSub Subtracts sparse updates from the variable referenced by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterSub  
tf.raw_ops.ResourceScatterSub(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] -= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   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 tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceScatterUpdate Assigns sparse updates to the variable referenced by resource.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterUpdate  
tf.raw_ops.ResourceScatterUpdate(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = updates[...]

# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]

 


 Args
  resource   A Tensor of type resource. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. A tensor of updated values to add to ref.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceSparseApplyAdadelta var: Should be from a Variable().  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyAdadelta  
tf.raw_ops.ResourceSparseApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False,
    name=None
)

 


 Args  
  var
  A Tensor of type resource.


  accum
  A Tensor of type resource. Should be from a Variable().


  accum_update
 
 A Tensor of type resource. Should be from a Variable(). 
 

  lr   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. Learning rate. Must be a scalar.  
  rho   A Tensor. Must have the same type as lr. Decay factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   


 


 Returns   The created Operation.